This project was designed to study the patterns of variability in fish populations. My hypothesis is that specific population patterns should be related to evolutionary concepts (phylogenetic patterns} , zoogeographic considerations (faunal patterns), and their vertical distributions. These patterns should be detected by comparing certain population parameters [growth parameters (K, LINF), the natural mortality coefficient (M) size at first maturity (LM), age at first maturity (TM), size at age 1 (L1) , the weight-length exponential coefficient (b) , and life span (T95)] which are intrinsic biological features of the population. Comparative methods were used to analyze data from published fish population studies by comparing fish population parameters, individually, in pairs (ratio or linear regression), or grouped together (discriminant analysis or Cooley and Lohnes' classification method), in order to find the similarities or differences among different categories, and then to group these into patterns. Published data provided 682 parameter records from 43 families (171 species) of fishes. My findings suggested that more satisfactory results would be obtained from a greater volume of data. Therefore, all the analyses were based mainly on 15 families with large sample sizes (Bothidae, Clupeidae, Cyprinidae, Engraulidae, Gadidae, Hiodontidae, Osmeridae, Percidae, Pleuronectidae, Salmonidae, Sciaenidae, Scombridae, Scorpaenidae, Sparidae, and Sgualidae). Sample sizes, mean values, standard errors, and coefficients of variation for population parameters and relative characters of the 15 families of fishes are listed in the summary table. These data would enable the extrapolation of results based on many areas for management of other fish stocks where data are lacking. In the majority of families significant linear regression relationships were found between 1/K--LINF, between LM--LINF, and between M--K. This means that fish having a greater asymptotic length (LINF) also have a larger size at first maturity (LM), a lower natural mortality coefficient (M), and a lower rate (K) at which the asymptotic length is reached. Using the F-test and the appropriate t-test as a basis for comparison of variances and means of individual parameters, it is evident that in most cases there are significant differences between families. This confirms one of my hypothesis; namely that differences between families, as shown by population parameters, exist from phylogenetic considerations. By comparing the four characters (K, LINF, LM, and LH/LINF) the fish families can be divided into the following groups: A) Shoaling pelagic fishes - Engraulidae, Clupeidae, and Osmeridae. These families have the highest K values (1.6 for Engraulidae, over 0.4 for the others), the smallest LINF, LM, and a very high LM/LINF ratio (over 0.7). B) Large pelagic fishes - Scombridae. This family has a moderately high K value (around 0.35) and the largest LINF. C) Demersal fishes - Gadidae, Pleuronectidae, Scorpaenidae, Sparidae etc. These families have low K values (less than 0.25), intermediate LINF size, and lower LM/LINF ratios (less than 0.6). D) Freshwater fish - Cyprinidae. This family has K and LINF values which are similar to those of the demersal fishes, but has a smaller LM length and, especially, the lowest LK/LINF (0.4) and TH/T95 (0.2) ratios. Stepwise discriminant analysis based on 7 variables in the 15 families showed that over 90% of the 620 cases considered independently could be correctly classified into the right families. Cooley and Lohnes' classification method was also utilized among species within 5 major families (Clupeidae, Cyprinidae, Gadidae, Pleuronectidae, and Scombridae). Correct classification ranged from 5 8.6% (Pleuronectidae) to 87.6% (Cyprinidae). These results further confirmed the existence of population patterns by examination of population parameters. Cluster analysis based on 7 population parameters displayed the closeness among the 15 families. Dendrograph relationships brought out the ecological, rather than the systematic, affinities between families. / Science, Faculty of / Zoology, Department of / Graduate

Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still several challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, most complex traits are suggested to be highly polygenic, i.e., they are affected by a vast number of risk variants with individually small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. Third, accumulating evidence from GWAS suggests the pervasiveness of pleiotropy, a phenomenon that some genetic variants can be associated with multiple traits, but there is a lack of unified framework which is scalable to reveal relationship among a large number of traits and prioritize genetic variants simultaneously with functional annotations integrated. In this thesis, we propose two statistical methods to address these challenges using integrative analysis of summary statistics from GWASs and functional annotations. In the first part, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization (EM) algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWASs of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes. In the second part, we propose a latent probit model (LPM) which combines summary statistics from multiple GWASs and functional annotations, to characterize relationship and increase statistical power to identify risk variants. LPM can also perform hypothesis testing for pleiotropy and annotations enrichment. To enable the scalability of LPM as the number of GWASs increases, we developed an efficient parameter-expanded EM (PX-EM) algorithm which can execute parallelly. We first validated the performance of LPM through comprehensive simulations, then applied it to analyze 44 GWASs with nine genic category annotations. The results demonstrate the benefits of LPM and can offer new insights of disease etiology.

Estimation of the sensitivity and specificity of cancer screening tests using data from population-level databases is complicated by the lack of independent confirmation of test results using a "gold standard''. The true sensitivity and specificity are unknown and errors in measurement can occur due to subjective clinical judgment, technical imperfections or interpretational differences. A further complication is clustered data (such as patients nested within examiners within screening centre), which are common in population-based screening. We propose a cancer screening model that accommodates the partially unobserved disease status, clustered data structures, general covariate effects, and the dependence between exams. The model is applied to the estimation of the diagnostic accuracy of mammography and clinical breast examination using a cohort consisting of women 50 to 69 years of age screened at the OBSP between January 1, 2002 and December 31, 2003. When offered in addition to mammography, we found CBE may benefit women using hormone therapy but not likely benefit women with dense breast tissues. The thesis also discusses two measures of interest, the length of the pre-clinical state and the false negative rate. Two estimation procedures are proposed to model the pre-clinical state duration, the false negative rate of screening exam, and the underlying incidence rate in the screened population. Both methods assume the sojourn time follows a negative exponential distribution, but we consider two different forms for the false negative rate: 1) constant with time and 2) an exponential function to compensate for the fact that lesions should become easier to detect the closer they are to become clinically evident. The proposed methods are illustrated with another cohort of women who were first screened through the OBSP between January 1, 2003, and December 31, 2004 and were followed up until December 31, 2009. / Thesis / Doctor of Philosophy (PhD)

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No description available.

published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / abstract / Mathematics / Master / Master of Philosophy

published_or_final_version / abstract / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy