LH and IGF-1 release during oestrus and early luteal phase in lactating and non-lactating horse mares

The aim of the present study was to determine effects of lactation on basal LH and IGF-1 concentrations and on the LH response to a GnRH-analogue at different stages of the oestrous cycle in mares. A total of 17 cyclic Haflinger mares were included in the study. Experiments were performed on lactating mares in first postpartum oestrus, the subsequent early luteal phase, and second postpartum oestrus. Non-lactating mares were used in oestrus and early luteal phase. Blood samples were taken for 1 h at 15 min intervals. Mares were then injected with the GnRH-analogue buserelin (GnRHa; 5 microg i.v.) and blood samples were drawn every 15 min for further 2 h. LH in all samples and basal IGF-1-concentrations were determined by RIA. In lactating mares, basal LH concentrations during the early luteal phase tended to be lower (p = 0.07) and the LH response to GnRHa, calculated as area under the curve, was significantly less pronounced compared to non-lactating mares (p < 0.01). As well in lactating mares, the basal LH concentration between first early luteal phase and second oestrus differed significantly (p < 0.05) and the net response to GnRHa was significantly lower between first oestrus as well as second oestrus and first early luteal phase (p < 0.05) but not between first and second oestrous postpartum. Within the group of non-lactating mares, the LH response to GnRHa was as well significantly lower during oestrus than during early luteal phase (p < 0.01). IGF-1 concentrations differed neither between groups nor stages of the cycle within groups. In conclusion, basal and GnRHa-stimulated LH release in lactating mares is lower than in non-lactating mares. This difference, however, occurs only in the early luteal phase. In lactating mares, concentrations of LH appear adequate to allow ovulation to occur.

TRIO LOGIC REGRESSION - DETECTION OF SNP - SNP INTERACTIONS IN CASE-PARENT TRIOS

Statistical approaches to evaluate higher order SNP-SNP and SNP-environment interactions are critical in genetic association studies, as susceptibility to complex disease is likely to be related to the interaction of multiple SNPs and environmental factors. Logic regression (Kooperberg et al., 2001; Ruczinski et al., 2003) is one such approach, where interactions between SNPs and environmental variables are assessed in a regression framework, and interactions become part of the model search space. In this manuscript we extend the logic regression methodology, originally developed for cohort and case-control studies, for studies of trios with affected probands. Trio logic regression accounts for the linkage disequilibrium (LD) structure in the genotype data, and accommodates missing genotypes via haplotype-based imputation. We also derive an efficient algorithm to simulate case-parent trios where genetic risk is determined via epistatic interactions.

Concordance Probability and Discriminatory Power in Proportional Hazards Regression

The concordance probability is used to evaluate the discriminatory power and the predictive accuracy of nonlinear statistical models. We derive an analytic expression for the concordance probability in the Cox proportional hazards model. The proposed estimator is a function of the regression parameters and the covariate distribution only and does not use the observed event and censoring times. For this reason it is asymptotically unbiased, unlike Harrell's c-index based on informative pairs. The asymptotic distribution of the concordance probability estimate is derived using U-statistic theory and the methodology is applied to a predictive model in lung cancer.

Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part aggress with the more general information bound calculations of Robins, Hsieh, and Newey (1995). By verifying the conditions of Murphy and Van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.