On the moderators of trait avoidance motivation in predicting cognitive biases and adjustment

The present research focused on motivational and personality traits measuring individual differences in the experience of negative affect, in reactivity to negative events, and in the tendency to avoid threats. In this thesis, such traits (i.e., neuroticism and dispositional avoidance motivation) are jointly referred to as trait avoidance motivation. The seven studies presented here examined the moderators of such traits in predicting risk judgments, negatively biased processing, and adjustment. Given that trait avoidance motivation encompasses reactivity to negative events and tendency to avoid threats, it can be considered surprising that this trait does not seem to be related to risk judgments and that it seems to be inconsistently related to negatively biased information processing. Previous work thus suggests that some variable(s) moderate these relations. Furthermore, recent research has suggested that despite the close connection between trait avoidance motivation and (mal)adjustment, measures of cognitive performance may moderate this connection. However, it is unclear whether this moderation is due to different response processes between individuals with different cognitive tendencies or abilities, or to the genuinely buffering effect of high cognitive ability against the negative consequences of high trait avoidance motivation. Studies 1-3 showed that there is a modest direct relation between trait avoidance motivation and risk judgments, but studies 2-3 demonstrated that state motivation moderates this relation. In particular, individuals in an avoidance state made high risk judgments regardless of their level of trait avoidance motivation. This result explained the disparity between the theoretical conceptualization of avoidance motivation and the results of previous studies suggesting that the relation between trait avoidance motivation and risk judgments is weak or nonexistent. Studies 5-6 examined threat identification tendency as a moderator for the relationship between trait avoidance motivation and negatively biased processing. However, no evidence for such moderation was found. Furthermore, in line with previous work, the results of studies 5-6 suggested that trait avoidance motivation is inconsistently related to negatively biased processing, implying that theories concerning traits and information processing may need refining. Study 7 examined cognitive ability as a moderator for the relation between trait avoidance motivation and adjustment, and demonstrated that cognitive ability moderates the relation between trait avoidance motivation and indicators of both self-reported and objectively measured adjustment. Thus, the results of Study 7 supported the buffer explanation for the moderating influence of cognitive performance. To summarize, the results showed that it is possible to find factors that consistently moderate the relations between traits and important outcomes (e.g. adjustment). Identifying such factors and studying their interplay with traits is one of the most important goals of current personality research. The present thesis contributed to this line of work in relation to trait avoidance motivation.

Quantitative trait loci detection and benefits from marker-assisted selection in dairy cattle

Knowing the chromosomal areas or actual genes affecting the traits under selection would add more information to be used in the selection decisions which would potentially lead to higher genetic response. The first objective of this study was to map quantitative trait loci (QTL) affecting economically important traits in the Finnish Ayrshire population. The second objective was to investigate the effects of using QTL information in marker-assisted selection (MAS) on the genetic response and the linkage disequilibrium between the different parts of the genome. Whole genome scans were carried out on a grand-daughter design with 12 half-sib families and a total of 493 sons. Twelve different traits were studied: milk yield, protein yield, protein content, fat yield, fat content, somatic cell score (SCS), mastitis treatments, other veterinary treatments, days open, fertility treatments, non-return rate, and calf mortality. The average spacing of the typed markers was 20 cM with 2 to 14 markers per chromosome. Associations between markers and traits were analyzed with multiple marker regression. Significance was determined by permutation and genome-wise P-values obtained by Bonferroni correction. The benefits from MAS were investigated by simulation: a conventional progeny testing scheme was compared to a scheme where QTL information was used within families to select among full-sibs in the male path. Two QTL on different chromosomes were modelled. The effects of different starting frequencies of the favourable alleles and different size of the QTL effects were evaluated. A large number of QTL, 48 in total, were detected at 5% or higher chromosome-wise significance. QTL for milk production were found on 8 chromosomes, for SCS on 6, for mastitis treatments on 1, for other veterinary treatments on 5, for days open on 7, for fertility treatments on 7, for calf mortality on 6, and for non-return rate on 2 chromosomes. In the simulation study the total genetic response was faster with MAS than with conventional selection and the advantage of MAS persisted over the studied generations. The rate of response and the difference between the selection schemes reflected clearly the changes in allele frequencies of the favourable QTL. The disequilibrium between the polygenes and QTL was always negative and it was larger with larger QTL size. The disequilibrium between the two QTL was larger with QTL of large effect and it was somewhat larger with MAS for scenarios with starting frequencies below 0.5 for QTL of moderate size and below 0.3 for large QTL. In conclusion, several QTL affecting economically important traits of dairy cattle were detected. Further studies are needed to verify these QTL, check their presence in the present breeding population, look for pleiotropy and fine map the most interesting QTL regions. The results of the simulation studies show that using MAS together with embryo transfer to pre-select young bulls within families is a useful approach to increase the genetic merit of the AI-bulls compared to conventional selection.

Joint Regression and Association Models for Repeated Categorical Responses

The focus of this study is on statistical analysis of categorical responses, where the response values are dependent of each other. The most typical example of this kind of dependence is when repeated responses have been obtained from the same study unit. For example, in Paper I, the response of interest is the pneumococcal nasopharengyal carriage (yes/no) on 329 children. For each child, the carriage is measured nine times during the first 18 months of life, and thus repeated respones on each child cannot be assumed independent of each other. In the case of the above example, the interest typically lies in the carriage prevalence, and whether different risk factors affect the prevalence. Regression analysis is the established method for studying the effects of risk factors. In order to make correct inferences from the regression model, the associations between repeated responses need to be taken into account. The analysis of repeated categorical responses typically focus on regression modelling. However, further insights can also be gained by investigating the structure of the association. The central theme in this study is on the development of joint regression and association models. The analysis of repeated, or otherwise clustered, categorical responses is computationally difficult. Likelihood-based inference is often feasible only when the number of repeated responses for each study unit is small. In Paper IV, an algorithm is presented, which substantially facilitates maximum likelihood fitting, especially when the number of repeated responses increase. In addition, a notable result arising from this work is the freely available software for likelihood-based estimation of clustered categorical responses.

Complex trait of acute pancreatitis : Studies of candidate genes and adipokines

Acute pancreatitis (AP) is a common disease. Mild disease resolves spontaneously in a few days. Severe forms of the disease can lead to local complications, necrosis, and abscesses in and around the pancreas. Systemic inflammation in severe AP is associated with distant organ failures. The aim of this study is to identify genetically determined prognostic factors involved in the clinical features of AP. The study employs a candidate-gene approach, and the genes are involved in trysinogen activation in the initiation phase of the disease, as well as in the systemic inflammation as the disease proceeds. The last study examines adipokines, fat-derived hormones characterized with the capacity to modify inflammation. SPINK 1 is a gene coding trypsin activation inhibitor. Mutations N34S and P55N were determined by minisequencing methods in 371 AP patients and in 459 controls. The mutation N34S was more common in AP patients (7.8%) than in controls (2.6%). This suggests that SPINK 1 gene mutation N34S is a risk factor for AP. In the fourth study, in 12 matched pairs of patients with severe and mild AP, levels of adipokines, adiponectin, and leptin were evaluated. Plasma adipokine levels did not differ between patients with mild and severe AP. The results suggest that in AP, adipokine plasma levels are not factors predisposing to organ failures. This study identified the SPINK 1 mutation N34S to be a risk factor for AP in the general population. As AP is a multifactorial disease, and extensive genetic heterogeneity is likely, further identification of genetic factors in the disease requires larger future studies with more advanced genetic study models. Further identification of the patient characteristics associated with organ failures offers another direction of the study to achieve more detailed understanding of the severe form of AP.

Categorical Meteorological Products: Evaluation and Analysis

In meteorology, observations and forecasts of a wide range of phenomena for example, snow, clouds, hail, fog, and tornados can be categorical, that is, they can only have discrete values (e.g., "snow" and "no snow"). Concentrating on satellite-based snow and cloud analyses, this thesis explores methods that have been developed for evaluation of categorical products and analyses. Different algorithms for satellite products generate different results; sometimes the differences are subtle, sometimes all too visible. In addition to differences between algorithms, the satellite products are influenced by physical processes and conditions, such as diurnal and seasonal variation in solar radiation, topography, and land use. The analysis of satellite-based snow cover analyses from NOAA, NASA, and EUMETSAT, and snow analyses for numerical weather prediction models from FMI and ECMWF was complicated by the fact that we did not have the true knowledge of snow extent, and we were forced simply to measure the agreement between different products. The Sammon mapping, a multidimensional scaling method, was then used to visualize the differences between different products. The trustworthiness of the results for cloud analyses [EUMETSAT Meteorological Products Extraction Facility cloud mask (MPEF), together with the Nowcasting Satellite Application Facility (SAFNWC) cloud masks provided by Météo-France (SAFNWC/MSG) and the Swedish Meteorological and Hydrological Institute (SAFNWC/PPS)] compared with ceilometers of the Helsinki Testbed was estimated by constructing confidence intervals (CIs). Bootstrapping, a statistical resampling method, was used to construct CIs, especially in the presence of spatial and temporal correlation. The reference data for validation are constantly in short supply. In general, the needs of a particular project drive the requirements for evaluation, for example, for the accuracy and the timeliness of the particular data and methods. In this vein, we discuss tentatively how data provided by general public, e.g., photos shared on the Internet photo-sharing service Flickr, can be used as a new source for validation. Results show that they are of reasonable quality and their use for case studies can be warmly recommended. Last, the use of cluster analysis on meteorological in-situ measurements was explored. The Autoclass algorithm was used to construct compact representations of synoptic conditions of fog at Finnish airports.