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Genetics affects not only the weight of piglets at birth but also the variability of birth weight within litter. Previous studies on this topic assigned the sample standard deviation of piglet birth weights within litter as an observation to the sow. However, the contribution of the difference in mean birth weight per sex on the within-litter variance has been neglected so far. This work deals with the genetic effect on within-litter variance when different statistical models with different distributional assumptions are used and considers the sex effect and appropriate weights per trait. Traits were formed from the pooled sample variance of male and female birth weights within litter. A linear model approach was fitted to the logarithmized within-litter variance and the sample standard deviation. A generalized linear model with gamma-distributed residuals and log-link function was applied to the untransformed sample variance. Models were compared by analysing data from 9439 litters from Landrace and Large White of a commercial breeding programme. The estimates of heritability for different traits ranged from 7% to 11%. Although the generalized linear mixed model is preferred from a mathematical view, the rank correlations between breeding values of the linear mixed models and the generalized linear mixed model were relatively high, i.e. 94% to 98%. By residual diagnostics, a linear mixed model using the weighted and pooled within-litter standard deviation was identified as most suitable.

Reduction in the variability of piglet birth weight within litter and increased piglet survival are key objective in schemes aiming to improve sow prolificacy. In previous studies, variation in birth weight was described by the sample standard deviation of birth weights within one litter, and the genetic impact has been proved. In this study, we additionally considered the sex effect on piglet's birth weight and on its variability. The sample variance of birth weights per litter separated by sex was assigned as a trait of the sow. Different transformations of the trait were fitted by linear and generalized linear mixed models. Based on 1111 litters from Landrace sows, the estimates of heritability for the different measures ranged from 11 to 12%. We analysed the influence of including birth weight of stillborn piglets on the variability of birth weight within litter. With omitted stillborns, the heritability was estimated approximately 2% higher than that in investigations of all born piglets, and the impact of sex on birth weight variability was increased. Because the proportion of intrapartum deaths is rather high, it is recommended to consider the total number of piglets born per litter when analysing birth weight variation., (© 2010 Blackwell Verlag GmbH.)

Weaning of the pig is generally regarded as a stressful event which could lead to clinical implications because of the changes in the intestinal ecosystem. The functional properties of microbiota inhabiting the pig's small intestine (SI), including lactobacilli which are assumed to exert health-promoting properties, are yet poorly described. Thus, we determined the ecophysiology of bacterial groups and within genus Lactobacillus in the SI of weaning piglets and the impact of dietary changes. The SI contents of 20 piglets, 4 killed at weaning (only sow milk and no creep feed) and 4 killed at 1, 2, 5, and 11 days post weaning (pw; cereal-based diet) were examined for bacterial cell count and bacterial metabolites by fluorescence in situ hybridization (FISH). Lactobacilli were the predominant group in the SI except at 1 day pw because of a marked reduction in their number. On day 11 pw, bifidobacteria and E. coli were not detected, and Enterobacteriaceae and members of the Clostridium coccoides/Eubacterium rectale cluster were only found occasionally. L. sobrius/L. amylovorus became dominant species whereas the abundance of L. salivarius and L. gasseri/johnsonii declined. Concentration of lactic acid increased pw whereas pH, volatile fatty acids, and ammonia decreased. Carbohydrate utilization of 76 Lactobacillus spp. isolates was studied revealing a shift from lactose and galactose to starch, cellobiose, and xylose, suggesting that the bacteria colonizing the SI of piglets adapt to the newly introduced nutrients during the early weaning period. Identification of isolates based on partial 16S rRNA gene sequence data and comparison with fermentation data furthermore suggested adaptation processes below the species level. The results of our study will help to understand intestinal bacterial ecophysiology and to develop nutritional regimes to prevent or counteract complications during the weaning transition.

Frequency analyses of EEG data yield large data sets, which are high-dimensional and have to be evaluated statistically without a large number of false positive statements. There exist several methods to deal with this problem in multiple comparisons. Knowing the number of true hypotheses increases the power of some multiple test procedures, however the number of true hypotheses is unknown, in general, and must be estimated. In this paper, we derive two new multiple test procedures by using an upper bound for the number of true hypotheses. Our first procedure controls the generalized family-wise error rate, and thus is an improvement of the step-down procedure of Hommel and Hoffmann [Hommel G., Hoffmann T. Controlled uncertainty. In: Bauer P. Hommel G. Sonnemann E., editors. Multiple Hypotheses Testing, Heidelberg: Springer 1987;ISBN 3540505598:p. 154-61]. The second new procedure controls the false discovery proportion and improves upon the approach of Lehmann and Romano [Lehmann E.L., Romano J.P. Generalizations of the familywise error rate. Ann. Stat. 2005;33:1138-54]. By Monte-Carlo simulations, we show how the gain in power depends upon the accuracy of the estimate of the number of true hypotheses. The gain in power of our procedures is demonstrated in an example using EEG data on the processing of memorized lexical items.

Quantitative trait loci (QTLs) may affect not only the mean of a trait but also its variability. A special aspect is the variability between multiple measured traits of genotyped animals, such as the within-litter variance of piglet birth weights. The sample variance of repeated measurements is assigned as an observation for every genotyped individual. It is shown that the conditional distribution of the non-normally distributed trait can be approximated by a gamma distribution. To detect QTL effects in the daughter design, a generalized linear model with the identity link function is applied. Suitable test statistics are constructed to test the null hypothesis H(0): No QTL with effect on the within-litter variance is segregating versus H(A): There is a QTL with effect on the variability of birth weight within litter. Furthermore, estimates of the QTL effect and the QTL position are introduced and discussed. The efficiency of the presented tests is compared with a test based on weighted regression. The error probability of the first type as well as the power of QTL detection are discussed and compared for the different tests.

In this paper, we address the problem of calculating power and sample sizes associated with simultaneous tests for non-inferiority. We consider the case of comparing several experimental treatments with an active control. The approach is based on the ratio view, where the common non-inferiority margin is chosen to be some percentage of the mean of the control treatment. Two power definitions in multiple hypothesis testing, namely, complete power and minimal power, are used in the computations. The sample sizes associated with the ratio-based inference are also compared with that of a comparable inference based on the difference of means for various scenarios. It is found that the sample size required for ratio-based inferences is smaller than that of difference-based inferences when the relative non-inferiority margin is less than one and when large response values indicate better treatment effects. The results are illustrated with examples., (Copyright 2005 John Wiley & Sons, Ltd.)

The generation of special crosses between different inbred lines such as recombinant inbred strains (RIS) and intermated recombinant inbred populations (IRIP) is being used to improve the power of QTL detection techniques, in particular fine mapping. These approaches acknowledge the fact that recombination of linked loci increases with every generation, caused by the accumulation of crossovers appearing between the loci at each meiosis. This leads to an expansion of the map distance between the loci. While the amount of the map expansion of RIS and IRIP is known for infinite inbred generations, it is not known for finite numbers of generations. This gap was closed here. Since the recursive evaluation of the map expansion factors turned out to be complex, a useful approximation was derived.

The experimental power of a granddaughter design to detect quantitative trait loci (QTL) in dairy cattle is often limited by the availability of progeny-tested sires, by the ignoring of already identified QTL in the statistical analysis, and by the application of stringent experimentwise significance levels. This study describes an experiment that addressed these points. A large granddaughter design was set up that included sires from two countries (Germany and France), resulting in almost 2000 sires. The animals were genotyped for markers on nine different chromosomes. The QTL analysis was done for six traits separately using a multimarker regression that included putative QTL on other chromosomes as cofactors in the model. Different variants of the false discovery rate (FDR) were applied. Two of them accounted for the proportion of truly null hypotheses, which were estimated to be 0.28 and 0.3, respectively, and were therefore tailored to the experiment. A total of 25 QTL could be mapped when cofactors were included in the model-7 more than without cofactors. Controlling the FDR at 0.05 revealed 31 QTL for the two FDR methods that accounted for the proportion of truly null hypotheses. The relatively high power of this study can be attributed to the size of the experiment, to the QTL analysis with cofactors, and to the application of an appropriate FDR.

Objectives: In this article, we illustrate and compare exact simultaneous confidence sets with various approximate simultaneous confidence intervals for multiple ratios as applied to many-to-one comparisons. Quite different datasets are analyzed to clarify the points., Methods: The methods are based on existing probability inequalities (e.g., Bonferroni, Slepian and Sidak), estimation of nuisance parameters and re-sampling techniques. Exact simultaneous confidence sets based on the multivariate t-distribution are constructed and compared with approximate simultaneous confidence intervals., Results: It is found that the coverage probabilities associated with the various methods of constructing simultaneous confidence intervals (for ratios) in manyto-one comparisons depend on the ratios of the coefficient of variation for the mean of the control group to the coefficient of variation for the mean of the treatments. If the ratios of the coefficients of variations are less than one, the Bonferroni corrected Fieller confidence intervals have almost the same coverage probability as the exact simultaneous confidence sets. Otherwise, the use of Bonferroni intervals leads to conservative results., Conclusions: When the ratio of the coefficient of variation for the mean of the control group to the coefficient of variation for the mean of the treatments are greater than one (e.g., in balanced designs with increasing effects), the Bonferroni simultaneous confidence intervals are too conservative. Therefore, we recommend not using Bonferroni for this kind of data. On the other hand, the plug-in method maintains the intended confidence coefficient quite satisfactorily; therefore, it can serve as the best alternative in any case.

In cross-over designs, individual sequences of treatments are applied to the animals. Within such designs it is possible that every treatment could modify the effect of the subsequent treatment applied to the same animal. We compared three cross-over designs each with three treatments, three periods, and two blocks. This comparison was done with respect to the variance of the estimations of the effects and its biases caused by the interactions between the treatment and the carry over effect of the foregoing treatment. Moreover, different methods of estimating variance components and calculating the degrees of freedom were compared by means of simulation. If the animal variance component is small, then the bias of the REML estimator of the variance components is greater than one of the widespread ANOVA-estimator called 'TYPE3'. But nevertheless, the mean squared error of this estimation is smaller in the case of REML in comparison to ANOVA. Therefore, the REML method should be preferred. For calculating the degrees of freedom, the Kenward-Roger method should be used. After applying this method, the true significance level is almost equal to its required value, but if the Satterthwaite method is used, the true significance level will be too high. If the interaction (treatment x carry over) is ignored in the model although it exists, the standard error of the treatment effect estimation is too great, and, therefore, the true significance level is too small. The methods which have been evaluated are available in the SAS-procedure MIXED (SAS Institute, 1999a). To assist the investigation of cross-over designs by using this software, we developed programs for data management and data analysis. These programs are available from the first author.

Genetic interference means that the occurrence of one crossover affects the occurrence and/or location of other crossovers in its neighborhood. Of the three components of genetic interference, two are well modeled: the distribution of the number and the locations of chiasmata. For the third component, chromatid interference, there exists only one model. Its application to real data has not yet been published. A further, new model for chromatid interference is presented here. In contrast to the existing model, it is assumed that chromatid interference acts only in the neighborhood of a chiasma. The appropriateness of this model is demonstrated by its application to three sets of recombination data. Both models for chromatid interference increased fit significantly compared to assuming no chromatid interference, at least for parts of the chromosomes. Interference does not necessarily act homogeneously. After extending both models to allow for heterogeneity of chromatid interference, a further improvement in fit was achieved.

HLA-G molecule is thought to play a major role in down-regulating the maternal immune response by inhibiting NK and T cell cytolytic activities. We examined the molecular regulatory mechanisms that may control the restricted expression pattern of the HLA-G gene. We first analyzed protein interactions between nuclear extracts from the HLA-G-positive JEG-3 choriocarcinoma and the HLA-G-negative NK-like YT2C2 cell lines to a 244 bp regulatory element located 1.2 kb from the HLA-G gene, previously shown to direct HLA-G expression in transgenic mouse placenta. This allowed characterization of cell-specific DNA-protein interactions that could account for differential cell-specific expression of the HLA-G gene. In particular two DNA-protein complexes were exclusively observed in YT2C2, suggesting that this HLA-G regulatory element is a target for putative cell-specific repressor factors. We further mapped nuclear factor binding sites to a 70 bp fragment in the upstream region of the regulatory element. We then investigated the effect of IFN-gamma on HLA-G gene expression. HLA-G cell surface expression was enhanced by IFN-gamma treatment in JEG-3 and U937 cell lines and peripheral blood monocytes while no effect was observed in tera-2 teratocarcinoma cell line. HLA-G transcriptional activity was increased only in JEG-3 and U937 cell lines. Activity of the 1.4-kb HLA-G promoter region was unchanged after IFN-gamma treatment in JEG-3 and Tera-2. These results suggest that both post-transcriptional and transcriptional mechanisms implicating IFN-responsive regulatory sequences outside the 1.4 kb-region are involved in IFN-gamma gene activation of the HLA-G gene.

HLA-G plays an essential role in feto-maternal tolerance by inhibiting lysis by maternal NK cells. The factors that allow tissue-specific activation of HLA-G gene expression in trophoblasts remain to be characterized. We investigated the potential effect of IL-10, a cytokine which is secreted in placenta, on HLA-G gene transcription in trophoblasts. Using Northern blot, RNase protection assay and RT-PCR analysis, we demonstrated that IL-10 enhances steady-state levels of HLA-G transcription in cultured trophoblast cells. We further tested the effect of IL-10 on HLA-G gene transcription and protein expression in peripheral blood monocytes, showing that IL-10 can up-regulate HLA-G cell surface expression in this cell type. This effect of IL-10 is selective, since classical MHC class I products and MHC class II are down-regulated in monocytes following IL-10 treatment. Induction of HLA-G expression by IL-10 on monocytes may thus play a role in down-regulation of the immune response. We propose that IL-10 secretion by trophoblasts during pregnancy may also influence the HLA class I expression pattern at the feto-maternal barrier, thus protecting the fetus from rejection. This should be taken into consideration in the design of treatment for pathologies of pregnancy.

Alleles at a microsatellite locus within the macrophage expressed lysozyme gene were shown to co-segregate with lysozyme activity in two half-sib families of Polish Black and White Lowland cattle. The bimodal distribution of lysozyme activities in both progeny groups is concordant with the occurrence of the alternative paternal alleles. The microsatellite is linked to a locus for high lysozyme activity that accounts for 70-95% of the phenotypic variation of both offspring groups considering the lysozyme activities of animals being older than 1 month.