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).

Digital spiral analysis for objective assessment of fine motor timing variability in Parkinson's disease

OBJECTIVES: To develop a method for objective assessment of fine motor timing variability in Parkinson’s disease (PD) patients, using digital spiral data gathered by a touch screen device. BACKGROUND: A retrospective analysis was conducted on data from 105 subjects including65 patients with advanced PD (group A), 15 intermediate patients experiencing motor fluctuations (group I), 15 early stage patients (group S), and 10 healthy elderly subjects (HE) were examined. The subjects were asked to perform repeated upper limb motor tasks by tracing a pre-drawn Archimedes spiral as shown on the screen of the device. The spiral tracing test was performed using an ergonomic pen stylus, using dominant hand. The test was repeated three times per test occasion and the subjects were instructed to complete it within 10 seconds. Digital spiral data including stylus position (x-ycoordinates) and timestamps (milliseconds) were collected and used in subsequent analysis. The total number of observations with the test battery were as follows: Swedish group (n=10079), Italian I group (n=822), Italian S group (n = 811), and HE (n=299). METHODS: The raw spiral data were processed with three data processing methods. To quantify motor timing variability during spiral drawing tasks Approximate Entropy (APEN) method was applied on digitized spiral data. APEN is designed to capture the amount of irregularity or complexity in time series. APEN requires determination of two parameters, namely, the window size and similarity measure. In our work and after experimentation, window size was set to 4 and similarity measure to 0.2 (20% of the standard deviation of the time series). The final score obtained by APEN was normalized by total drawing completion time and used in subsequent analysis. The score generated by this method is hence on denoted APEN. In addition, two more methods were applied on digital spiral data and their scores were used in subsequent analysis. The first method was based on Digital Wavelet Transform and Principal Component Analysis and generated a score representing spiral drawing impairment. The score generated by this method is hence on denoted WAV. The second method was based on standard deviation of frequency filtered drawing velocity. The score generated by this method is hence on denoted SDDV. Linear mixed-effects (LME) models were used to evaluate mean differences of the spiral scores of the three methods across the four subject groups. Test-retest reliability of the three scores was assessed after taking mean of the three possible correlations (Spearman’s rank coefficients) between the three test trials. Internal consistency of the methods was assessed by calculating correlations between their scores. RESULTS: When comparing mean spiral scores between the four subject groups, the APEN scores were different between HE subjects and three patient groups (P=0.626 for S group with 9.9% mean value difference, P=0.089 for I group with 30.2%, and P=0.0019 for A group with 44.1%). However, there were no significant differences in mean scores of the other two methods, except for the WAV between the HE and A groups (P<0.001). WAV and SDDV were highly and significantly correlated to each other with a coefficient of 0.69. However, APEN was not correlated to neither WAV nor SDDV with coefficients of 0.11 and 0.12, respectively. Test-retest reliability coefficients of the three scores were as follows: APEN (0.9), WAV(0.83) and SD-DV (0.55). CONCLUSIONS: The results show that the digital spiral analysis-based objective APEN measure is able to significantly differentiate the healthy subjects from patients at advanced level. In contrast to the other two methods (WAV and SDDV) that are designed to quantify dyskinesias (over-medications), this method can be useful for characterizing Off symptoms in PD. The APEN was not correlated to none of the other two methods indicating that it measures a different construct of upper limb motor function in PD patients than WAV and SDDV. The APEN also had a better test-retest reliability indicating that it is more stable and consistent over time than WAV and SDDV.

Design and application of experimental methods for steel sheet shearing

Shearing is the process where sheet metal is mechanically cut between two tools. Various shearing technologies are commonly used in the sheet metal industry, for example, in cut to length lines, slitting lines, end cropping etc. Shearing has speed and cost advantages over competing cutting methods like laser and plasma cutting, but involves large forces on the equipment and large strains in the sheet material. The constant development of sheet metals toward higher strength and formability leads to increased forces on the shearing equipment and tools. Shearing of new sheet materials imply new suitable shearing parameters. Investigations of the shearing parameters through live tests in the production are expensive and separate experiments are time consuming and requires specialized equipment. Studies involving a large number of parameters and coupled effects are therefore preferably performed by finite element based simulations. Accurate experimental data is still a prerequisite to validate such simulations. There is, however, a shortage of accurate experimental data to validate such simulations. In industrial shearing processes, measured forces are always larger than the actual forces acting on the sheet, due to friction losses. Shearing also generates a force that attempts to separate the two tools with changed shearing conditions through increased clearance between the tools as result. Tool clearance is also the most common shearing parameter to adjust, depending on material grade and sheet thickness, to moderate the required force and to control the final sheared edge geometry. In this work, an experimental procedure that provides a stable tool clearance together with accurate measurements of tool forces and tool displacements, was designed, built and evaluated. Important shearing parameters and demands on the experimental set-up were identified in a sensitivity analysis performed with finite element simulations under the assumption of plane strain. With respect to large tool clearance stability and accurate force measurements, a symmetric experiment with two simultaneous shears and internal balancing of forces attempting to separate the tools was constructed. Steel sheets of different strength levels were sheared using the above mentioned experimental set-up, with various tool clearances, sheet clamping and rake angles. Results showed that tool penetration before fracture decreased with increased material strength. When one side of the sheet was left unclamped and free to move, the required shearing force decreased but instead the force attempting to separate the two tools increased. Further, the maximum shearing force decreased and the rollover increased with increased tool clearance. Digital image correlation was applied to measure strains on the sheet surface. The obtained strain fields, together with a material model, were used to compute the stress state in the sheet. A comparison, up to crack initiation, of these experimental results with corresponding results from finite element simulations in three dimensions and at a plane strain approximation showed that effective strains on the surface are representative also for the bulk material. A simple model was successfully applied to calculate the tool forces in shearing with angled tools from forces measured with parallel tools. These results suggest that, with respect to tool forces, a plane strain approximation is valid also at angled tools, at least for small rake angles. In general terms, this study provide a stable symmetric experimental set-up with internal balancing of lateral forces, for accurate measurements of tool forces, tool displacements, and sheet deformations, to study the effects of important shearing parameters. The results give further insight to the strain and stress conditions at crack initiation during shearing, and can also be used to validate models of the shearing process.