Genetic heterogeneity of residual variance : estimation of variance components using double hierarchical generalized linear models

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms. Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model. Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

Detecting Major Genes Controlling Robustness of Chicken Body Weight Using Double Generalized Linear Models

Detecting both the majors genes that control the phenotypic mean and those controlling phenotypic variance has been raised in quantitative trait loci analysis. In order to mapping both kinds of genes, we applied the idea of the classic Haley-Knott regression to double generalized linear models. We performed both kinds of quantitative trait loci detection for a Red Jungle Fowl x White Leghorn F2 intercross using double generalized linear models. It is shown that double generalized linear model is a proper and efficient approach for localizing variance-controlling genes. We compared two models with or without fixed sex effect and prefer including the sex effect in order to reduce the residual variances. We found that different genes might take effect on the body weight at different time as the chicken grows.

Characterization of residential chimney conditions for flue gas flow measurements

A literature survey and a theoretical study were performed to characterize residential chimney conditions for flue gas flow measurements. The focus is on Pitot-static probes to give sufficient basis for the development and calibration of a velocity pressure averaging probe suitable for the continuous dynamic (i.e. non steady state) measurement of the low flow velocities present in residential chimneys. The flow conditions do not meet the requirements set in ISO 10780 and ISO 3966 for Pitot-static probe measurements, and the methods and their uncertainties are not valid. The flow velocities in residential chimneys from a heating boiler under normal operating condi-tions are shown to be so low that they in some conditions result in voiding the assumptions of non-viscous fluid justifying the use of the quadratic Bernoulli equation. A non-linear Reynolds number dependent calibration coefficient that is correcting for the viscous effects is needed to avoid significant measurement errors. The wide range of flow velocity during normal boiler operation also results in the flow type changing from laminar, across the laminar to turbulent transition region, to fully turbulent flow, resulting in significant changes of the velocity profile during dynamic measurements. In addition, the short duct lengths (and changes of flow direction and duct shape) used in practice are shown to result in that the measurements are done in the hydrodynamic entrance region where the flow velocity profiles most likely are neither symmetrical nor fully developed. A measurement method insensitive to velocity profile changes is thus needed, if the flow velocity profile cannot otherwise be determined or predicted with reasonable accuracy for the whole measurement range. Because of particulate matter and condensing fluids in the flue gas it is beneficial if the probe can be constructed so that it can easily be taken out for cleaning, and equipped with a locking mechanism to always ensure the same alignment in the duct without affecting the calibration. The literature implies that there may be a significant time lag in the measurements of low flow rates due to viscous effects in the internal impact pressure passages of Pitot probes, and the significance in the discussed application should be studied experimentally. The measured differential pressures from Pitot-static probes in residential chimney flows are so low that the calibration and given uncertainties of commercially available pressure transducers are not adequate. The pressure transducers should be calibrated specifically for the application, preferably in combination with the probe, and the significance of all different error sources should be investigated carefully. Care should be taken also with the temperature measurement, e.g. with averaging of several sensors, as significant temperature gradients may be present in flue gas ducts.

Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Strategisk utveckling och ekonomistyrning : Ett livscykelperspektiv

This dissertation synthesizes previous research and develops a model for the study of strategic development, strategic congruence and management control. The model is used to analyze a longitudinal case study of the Swedish engineering company Atlas Copco. Employing contingency theory, the study confirms that long-term survival of a company requires adaption to contingencies. Three levels of strategy are examined: corporate, business and functional. Previous research suggests that consistency between these levels (strategic congruence) is necessary for a company to be competitive. The dissertation challenges this proposition by using a life-cycle perspective and analyzes strategic congruence in the different phases of a life cycle. It also studies management control from a life-cycle perspective. In this context, two types of management control are examined: formal and informal. From a longitudinal perspective, the study further discusses how these types interact during organizational life cycles. The dissertation shows that strategic development is more complex than previous studies have indicated. It is a long, complex and non-linear process, the results of which cannot always be predicted. Previous models for strategy and management control are based on simple relationships and rarely take into account the fact that companies often go through different phases of strategic development. The case study shows that strategic incongruence may occur at times during organizational life cycles. Furthermore, the use of management control varies over time. In the maturity phase, formal control is in focus, while the use of informal control has a bigger role in both the introduction and decline phases. Research on strategy and management control has intensified in recent years. Still there is a gap regarding the coordination of complex corporate structures. The present study contributes with further knowledge on how companies manage long-term strategic development. Few studies deal with more than two levels of strategy. Moreover, the present study addresses the need to understand strategic congruence from a life-cycle perspective. This is particularly relevant in practice, when management in large companies face difficult issues for which they expect business research to assist them in the decision-making process.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.