Submitted by Eunice Novais (enovais@uepg.br) on 2018-04-25T18:05:26Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Michelli Sieklicki.pdf: 1236462 bytes, checksum: de51a11749361af8058466a0d8ddcb71 (MD5) / Made available in DSpace on 2018-04-25T18:05:26Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Michelli Sieklicki.pdf: 1236462 bytes, checksum: de51a11749361af8058466a0d8ddcb71 (MD5) Previous issue date: 2018-03-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O objetivo deste estudo foi avaliar a estrutura populacional e endogâmica dos rebanhos da raça Holandesa no estado do Paraná, a fim de verificar o processo de distribuição dos genes entre os animais ao longo dos anos. Os dados utilizados foram fornecidos pela Associação Paranaense dos Criadores da Raça Holandesa – APCBRH, com arquivo de pedigree de 206.796 animais, nascidos entre 1970 a 2014. Foram obtidos resultados para integridade do pedigree, pedigree completo, número efetivo de fundadores (fe) e número efetivo de ancestrais (fa), intervalo de gerações (IG), coeficiente de endogamia (F), tamanho efetivo de população (Ne), coeficiente médio de relação (CR) e número de progênies por reprodutor. Para o cálculo dos parâmetros populacionais e endogamia foram utilizados os softwares POPREP e ENDOG v.4.5. Com base no conjunto de dados, o pedigree completo apresentou animais com parentesco conhecido até a sexta geração. O intervalo de geração médio na população foi de 6,3 anos. Para o coeficiente médio de endogamia o resultado foi de 4,99%. Ne variou durante os períodos, variando de 22 a 114, enquanto a taxa de endogamia existente nestes mesmos períodos mostrou uma tendência decrescente nos últimos anos até 2014. Para o resultado de CR foi estimado em 0,71%. O número efetivo de fundadores (fe) foi de 418 animais e ancestrais (fa) foram 400 animais. Adicionalmente, foram identificados os reprodutores com maior número de progênies na população. De acordo com o nível de endogamia apresentado, pôde-se observar que disseminação do material genético dos principais reprodutores entre as gerações, resultou em bons indicadores de diversidade genética, o que permitiu propiciar uma boa estruturação genética dos rebanhos, facilitando a condução dos programas de seleção genética da raça, no referido estado. / The objective of the present study was to evaluate the population structure and inbreeding of Holstein herds in the state of Paraná. The data used were provided by the Holstein Association of Paraná (APCBRH), which included a pedigree file of 206,796 animals born between 1970 and 2014. Results were obtained pertaining to pedigree integrity, complete pedigree, effective number of founders (f) and effective number of ancestors (fa), generation interval (GI), inbreeding coefficient (F), size effective population (Ne), mean ratio coefficient (CR), and number of progenies per breeder. The POPREP and ENDOG v.4.5 software were used to calculate the relevant population parameters and inbreeding. Based on the data set, the pedigree completeness presented animals with known relationship up to a sixth generation. The mean generation interval in the population was 6.3 years. For the average inbreeding coefficient, the result was 4.99%. Ne varied during the periods, ranging from 22 to 114, while the rate of inbreeding in these same periods showed a decreasing trend in the last years until 2014. The CR was estimated to be 0.71%. The effective number of founders (fe) was 418 animals and ancestors (fa) were 400 animals. In addition, the reproducers with the highest number of progenies in the population were identified. According to the level of inbreeding presented, it was observed that the dissemination of the genetic material of the main breeders between the generations, resulted in good indicators of genetic diversity, which allowed to propitiate a good genetic structuring of the herds, facilitating the conduction of the breeding programs genetic selection of the breed in the said state.

The question “who infected whom” is a perennial one in the study of infectious disease dynamics. To understand characteristics of infectious diseases such as how many people will one case produce over the course of infection (the reproductive number), how much time between the infection of two connected cases (the generation interval), and what factors are associated with transmission, one must ascertain who infected whom. The current best practices for linking cases are contact investigations and pathogen whole genome sequencing (WGS). However, these data sources cannot perfectly link cases, are expensive to obtain, and are often not available for all cases in a study. This lack of discriminatory data limits the use of established methods in many existing infectious disease datasets. We developed a method to estimate the relative probability of direct transmission between any two infectious disease cases. We used a subset of cases that have pathogen WGS or contact investigation data to train a model and then used demographic, spatial, clinical, and temporal data to predict the relative transmission probabilities for all case-pairs using a simple machine learning algorithm called naive Bayes. We adapted existing methods to estimate the reproductive number and generation interval to use these probabilities. Finally, we explored the associations between various covariates and transmission and how they related to the associations between covariates and pathogen genetic relatedness. We applied these methods to a tuberculosis outbreak in Hamburg, Germany and to surveillance data in Massachusetts, USA. Through simulations we found that our estimated transmission probabilities accurately classified pairs as links and nonlinks and were able to accurately estimate the reproductive number and the generation interval. We also found that the association between covariates and genetic relatedness captures the direction but not absolute magnitude of the association between covariates and transmission, but the bias was improved by using effect estimates from the naive Bayes algorithm. The methods developed in this dissertation can be used to explore transmission dynamics and estimate infectious disease parameters in established datasets where this was not previously feasible because of a lack of highly discriminatory information, and therefore expand our understanding of many infectious diseases.