One of the most important aspects of genetic evaluation (GE) is the definition of contemporary groups (CG), commonly defined as animals of the same sex born in the same herd, year and season. The objective of this study was to use an aridity index (AI) to classify season and evaluate the implications on the GE of Braunvieh cattle. A data set with 32 777 and 22 448 birth weight (BW) and weaning weight adjusted to 240 days (WW) records, respectively, was used to compare two methods of classification of climatic seasons to be used in the definition of CG for GE models. The first method considered rain season criterion (RC), and the second method is a proposed classification using an AI. Both methods were compared using two approaches. The first approach examined differences in mixed models using the RC and AI season to select the best model for BW and WW, evaluated by different goodness of fit measures. The second approach considered fitting a GE model including the season classifications into the CG structure. Lower probability values for season effect and better goodness of fit measures were obtained when the season was classified according to the AI. Results showed that although differences are small, the AI allows a better model fitting for live-weight traits than RC and revealed a re-ranking effect on expected progeny differences data. Further analysis with other traits would demonstrate the extended utility of AI indicators to be considered for fitting models under a climatic change environment.

Aim: The present study aims to estimate the relative importance of genetic and environmental factors for health-related quality of life (HRQL) measured by the 12-item Short-Form Health Survey (SF-12). Methods: The study was based on two Danish twin cohorts (46,417 twin individuals) originating from the nationwide, population-based Danish Twin Registry. The twins were approached by a mailed-out questionnaire in 2002. The questionnaire included the SF-12, information on demographic factors, and questions on a variety of specific diseases. Heritability of the SF-12 includes the physical component summary (PCS) and the mental component summary (MCS); and etiologically important variance components were estimated using multivariate biometric models. The respondents were stratified into six groups, based on age and sex. Results: A total of 33,794 (73%) individual twins responded to the survey. The SF-12 was completed by 29,619 individuals, which included 9,120 complete twin pairs. Overall, the best-fitting model explaining the variance of HRQL was the ACE model. The estimated heritability of the SF-12 was between 11% and 35%, whereas between 65% and 89% could be explained by unique environmental or stochastic factors in the different sex and age groups. The highest heritability was seen among older twins. In addition, the genetic correlation between MCS and PCS scores was low (0.07 and 0.23 for males and females, respectively) among younger and high (0.26 and 0.45 for males and females, respectively) in the oldest age group. Both the largest genetic influence on HRQL and the largest genetic overlap between the scores were seen in the oldest age group, which consisted of twins older than 55. The unique environmental correlation between MCS and PCS were generally negative. Conclusion: The heritability of HRQL differs between different age groups. In general, most of the variance in the SF-12 summary components was determined by unique environmental factors.

Random regression models have been advocated for the analysis of test day records in dairy cattle. The effectiveness of a random regression analysis depends on the function used to model the data. To investigate functions suitable for the analysis of daily milk yield, test day milk yields of 7860 first lactation Holstein Friesian cows were analysed using random regression models involving three types of curves. Each analysis fitted the same curve to model overall trends through a fixed regression and random deviations due to animals. Curves included orthogonal polynomials, fitted to order 3 (quadratic), 4 (cubic) and 5 (quartic), respectively, a three-parameter parametric curve and a five-parameter parametric curve. Sets of random regression coefficients were fitted to model both animals’ genetic effects and permanent environmental effects. Temporary measurement errors were assumed independently but heterogeneously distributed, and assigned to one of 12 classes. Results showed that the measurement error variances were generally lowest around peak lactation, and higher at the beginning and end of lactation. Parametric curves yielded the highest likelihoods, but produced negative genetic associations between yield in early lactation and later lactation yields, while positive genetic correlations across the entire lactation were estimated with all models involving orthogonal polynomials. The fit of models using orthogonal polynomials to model test day yield was improved by including higher order fixed regressions.

The value of using a marker for a quantitative trait locus (QTL) affecting a sex-limited trait in an outbred poultry breeding nucleus was studied. Marker and QTL were in linkage equilibrium in the base population. The recombination rate between marker and QTL was 0-05. A closed nucleus with 9000 chickens per generation was deterministically simulated. The genetic model contained polygenes and a QTL linked to a marker. Genetic effects explained proportionately 0·3 of the phenotypic variance before selection. Under selection, polygenic variance reached an equilibrium and QTL variance decreased continuously over time. Cocks were selected in two steps. First the best cocks of each full-sib family were selected (within-family selection) while final selection took place after information on fiill-sibs was available. Hens were selected after they had completed production. The effect of using marker information in estimating breeding values was studied in an ongoing breeding programme. Transmission of marker alleles was always traceable. Cumulative response over five generations increased proportionately by 0·06 to 0·13 if a marker linked to a QTL that explained 0·2 of the genetic variance was used. Cumulative response increased up to 0·28 if the QTL explained 0-8 of the genetic variance. Additional response due to the use of a marker increased with increasing intensity of within-family selection of cocks, increased with increasing variance explained by the QTL and was higher if within-family selection of cocks was carried out after rather than before their sibs had complete records.

A method for the iterative set-up of optimum designs for crossbreeding experiments was used to study the robustness of designs to differences in the biological interpretation of two-locus epistatic interaction. Designs could be found which are efficient for the estimation of genetic models including, alternatively, seven different types of epistatic effects. Also, the design efficiency of a large-scale beef cattle crossbreeding experiment between Angus and Hereford cattle conducted at the Clay Center, Nebraska, and reported by Koch, Dickerson, Cundiff and Gregory (1985) was investigated and found to be high (proportionately 0·88 of the optimum). It was concluded that choice of the right genetic groups (i.e. types of crossbreds) seems to be more important for a good design than the exact number of observations allocated to each group.

We consider three alternative parametric models to describe genetic and environmental influences on smoking initiation and smoking persistence. Under the single liability dimension model, the same genetic and environmental influences which determine smoking initiation also influence smoking persistence. Under the independent liability dimensions model, independent initiation and persistence dimensions determine onset of smoking, and persistence in those who become smokers. The combined model also postulates separate initiation and persistence dimensions, but allows for the possibility that some smokers are so low on liability to smoke on the initiation dimension that they become ex-smokers for this reason. Reanalyses of London twin data published by Eaves and Eysenck support the single liability dimension model. We discuss the difficulty of reconciling this finding with the hypothesis that nicotine dependence is a major determinant of smoking persistence, but caution that sample sizes in the London twin study were small.

In fitting genetic models on the basis of observations on an interrelated structure, sequential procedures can enhance efficiency. In this paper we consider the case of a rare gene segregating in a single large pedigree. The sampling rule is dictated by the effect of observations on the genotypic probability distribution of unobserved relatives; this effect is investigated.

This article discusses certain aspects of determining heritability estimates from the genetic analyses of data. First, a brief review is given of the major statistical techniques for fitting models to quantitative family data. Then, by reference to a general schematic framework, the basic models that have been proposed for such data will be described. Some new environmental models are also developed. Finally, by reference to some data sets, the interactive approach to data analysis is proposed, advocating the determination of adequacy of fit of all reasonable models to available data.

Twin data on bipolar and unipolar affective disorders are analyzed by multiple threshold models of inheritance. The two illness types are represented in the models on a continuum of genetic–environmental liability in which bipolar illness has a higher liability threshold than unipolar disorder. Autosomal single major locus model provides an acceptable fit to observed concordance rates in monozygotic twins. The multifactorial-polygenic model is rejected.

Let {XN(t), t ≧ 0}, N = 1, 2, … be a sequence of continuous-parameter Markov processes, and let TN(t)f(x) = Ex[f(XN(t))]. Suppose that limN→∞TN(t)f(x)= T(t)f(x), and that convergence is uniform over x and over t ∈ [0, K] for all K < ∞. When is convergence uniform over t ∈ [0, ∞)? Questions of this type are considered under the auxiliary condition that T(t)f(x) converges uniformly over x as t → ∞. A criterion for such ergodicity is given for semigroups T(t) associated with one-dimensional diffusions. The theory is illustrated by applications to genetic models.

Let w1, w12 and w2 be the fitnesses of genotypes A1A1, A1A2 and A2A2 in an infinite diploid population, and let pn be the A1, gene frequency in the nth generation. If fitness varies independently from generation to generation, then pn is a Markov process with a continuum of states. If E[In(wi/w12)] < 0 for i = 1, 2, then there is a unique stationary probability, and the distribution of pn converges to it as n → ∞.

For every N ≧ 1, let be a real-valued stationary process, and let . Suppose that and var, where ∊N and τN are positive null sequences. Limiting distributions of as N → ∞ are obtained for the cases τN = ∊N and τN = o(∊N). These results are established by an extension of a method due to Moran. The theory is illustrated by a variety of applications to genetic models.