no abstract provided by author / Master of Science

The popular sires are not always the greatest sires, for in many cases popular sites are not capable of transmitting average high production to their daughters. The true measure of a sire’s merit is the high average production of all his daughters, rather than the total number of Advanced Register daughters. The two greatest foundation cows of the Guernsey breed were May Rose II and Itchen Daisy III. The four great families of the Guernsey breed, in order of their importance, are as follows: May Rose, Masher, Governor of the Chene, and Sheet Anchor. The three greatest sires of the breed, when judged by the high production of their daughters, are King of the May, Ne Plus Ultra, and Golden Secret of Lilyvale. Select your herd sire from one of the best families, and one that is backed by average high production and individual merit. / Master of Science

A total of 1926 A.I. sampled Holstein bulls born from 1984 to 1988 and with first proofs from Summer 1991 to Summer 1993 were used to determine the accuracy of predicting PTAT and DTD from different sources of pedigree information obtained before the bull had daughter information. Pedigree sources used were PA, PI, PTAT_SIRE, and PTAT_DAM. Simple linear regression was used to determine which pedigree source predicted PTAT or DTD with the highest accuracy (highest R²). R² was higher for PA than had the other pedigree sources. R²s for PA to predict initial PTAT and DTD with daughter information were .59 and .18 respectively. Higher weights and R²s for PTAT than DTD resulted from the part whole relationship between PA and PTAT. Accuracy of prediction varied depending on when a bull received his first proof. R² values for PA to predict initial PTAT ranged from .35 to .69, and increased as the time of the pedigree estimate approached the date of the initial proof. R² values for PA to predict initial DTD ranged from .16 to .21 and increased as the time of the pedigree estimate approached the date of the initial proof. The impact of the within herd variance correction which was implemented in the Summer 1993 summary was also evaluated. Correlations between PA, PI, PTAT_SIRE, and PTAT_DAM from the Winter 93 and Summer 93 evaluations were .98, .99, .99, and .96 respectively. Regression of the change in DTD estimated from previous PA minus actual DTD on PTAT_DAM S 93 - PTAT_DAM W 93 for bulls grouped by date of initial proof gave R²s from .00 to .06. It was concluded that the variance correction had little impact on the dam’s of bulls in this study. The impact of the addition of granddaughters (son’s daughters) on the PTA of the bull dam was evaluated. The mean change in PTAT_DAM with the addition of first granddaughters was .016, indicating that the PTAT of the bull dam was slightly underestimated. R²s for the regression of the change in bull dam’s PTAT on DTD, DTD-PA, and PTAT-PA were .39, .54, and .56 respectively. Little evidence was found to indicate a bias based on the testing population used to prove the bull. R²s for the regression of PA and PI on PTAT from the bull’s initial proof with daughter information ranged from .38 to .69, and .26 to .58 respectively. When PA and PI were used to estimate PTAT of a bull’s second proof both within and across NAAB codes, values agreed closely. / Master of Science

A total of 1,644 A.I. sampled bulls born from 1984 to 1986 with first proofs from Winter 90 to Summer 91 were used to determine the accuracy of predicting DYD and PTA from different sources of pedigree information obtained before the bull had daughter information. Traits evaluated were milk, fat and protein. Pedigree sources considered were PA, PI, PTA_SIRE and PTA_DAM. Approximate weighted regression was used to determine which pedigree source predicted DYD or PTA with the highest accuracy (highest R²). For all traits, PA had a higher R² for DYD and PTA than PI. Regression coefficients were less than one for PA and PI. R² values for PA to predict first DYD milk, fat and protein were .17, .20 and .18, respectively. R² for PA to predict first PTA milk, fat and protein were .47, .54 and .49, respectively. Adding PTA_DAM to the model with PTA_SIRE resulted in a higher R² than the model with PTA_SIRE alone. As expected R² values were similar for PA and the model with PTA_SIRE and PTA_DAM. However, the weights for PTA_SIRE and PTA_DAM were less than .5. Higher weights and R²s for predicting PTA compared to predicting DYD resulted from the part-whole relationship between bull’s PTA and his PA. Overall, weights and R² were less than expected, but reasonable accuracy was obtained in estimating a young bull’s DYD and PTA from pedigree estimates. Accuracy of prediction varied depending on when the bull received his first proof. R² values of different groups of bulls based on the date of first DYD and PTA ranged from .06 to .20, .08 to .15 and .05 to .12 for predicting first DYD from PA for milk, fat and protein, respectively. Prediction accuracy in some groups of bulls was less possibly because of the limited number of sires and reduced variation in sire PTAs. Changes in evaluation procedures to expand the variance of extended records and to account for differences in within herd variance may have adversely affected the accuracy of prediction. The impact of the addition of granddaughters (son’s daughters) on the PTA of the dam was evaluated. Addition of granddaughter information decreased the average of dam’s PTA 70 kg, indicating the dams’ PTAs were generally inflated. Granddaughter information measured relative to PA of the son was useful to predict the change in the dam’s PTA at the AM evaluation the dam’s sons received first proofs. Regression coefficients ranged from .30 to .39, which were similar to the weights for w₃ in the PTA function. R for the regressions ranged from .33 to .72. Predicting further change in dam’s PTA (after the AM evaluation first granddaughter information was received) resulted in lower R? (.13 to .35) for additional granddaughter information. Evidence of bias and/or errors were found in bulls sampled outside the respective A.I. organizations’ designated sampling herds. These bulls had PAs that overestimated their DYDs for milk, fat and protein by 107 kg, 7.5 kg and 5.7 kg, respectively. The PAs of these bulls overestimated the PTAs by 97 kg, 6.8 kg and 4.5 kg for milk, fat and protein, respectively. Discrepancies were also found between average PTAs and DYDs and the PAs of bulls based on the rank of the dam’s PTA. Bulls from dams with lower PTAs tended to have PAs that underestimated their DYDs by 48 kg and .5 kg for milk and fat, respectively. These bulls had PAs that underestimated their PTAs for milk, fat and protein by 42 kg, .5 kg and .6 kg, respectively. Examination of bulls from high ranking dams for PTA milk, fat or protein revealed that bulls from dams with higher PTAs tended to have PAs that overestimated their DYDs by 65 kg, 5.3 kg and 4.5 kg for milk, fat and protein, respectively. The PAs of these bulls overestimated their PTAs by 49 kg, 4.2 kg and 2.9 kg, for milk, fat and protein, respectively. / Master of Science

Parentage analysis is concerned with the estimation of a sample’s pedigree structure, which is often essential knowledge for estimating population parameters of animal species, such as reproductive success. While it is often easy to relate one parent to an offspring simply by observation, the second parent remains frequently unknown. Parentage analysis uses genotypic data to estimate the pedigree, which then allows inferring the desired parameters. There are several software applications available for parentage analysis, one of which is MasterBayes, an extension to the statistical software package R. MasterBayes makes use of behavioural, phenotypic, spatial and genetic data, providing a Bayesian approach to simultaneously estimate pedigree and population parameters of interest, allowing for a range of covariate models. MasterBayes however assumes the sample to be a randomly collected from the population of interest. Often however, collected data will come from nests or otherwise from groups that are likely to contain siblings. If siblings are present, the assumption of a random population sample is not met anymore and as a result, the parameter variance will be underestimated. This thesis presents four methods to adjust MasterBayes’ parameter estimate to the presence of siblings, all of which are based on the pedigree structure, as estimated by MasterBayes. One approach, denoted as DEP, provides a Bayesian estimate, similar to MasterBayes’ approach, but incorporating the presence of siblings. Three further approaches, denoted as W1, W2 and W3, apply importance sampling to re-weight parameter estimates obtained from MasterBayes and DEP. Though fully satisfying adjustment of the estimate’s variance is only achieved at nearly perfect pedigree assignment, the presented methods do improve MasterBayes’ parameter estimation in the presence of siblings considerably, when the pedigree is uncertain. DEP and W3 show to be the most successful adjustment methods, providing comparatively accurate, though yet underestimated variances for small family sizes. W3 is the superior approach when the pedigree is highly uncertain, whereas DEP becomes superior when about half of all parental assignments are correct. Large family sizes introduce to all approaches a tendency to underestimate the parameter variance, the degree of underestimation depending on the certainty of pedigree. Additionally, the importance sampling schemes provide at large uncertainty of pedigree comparatively good estimates of the parameter’s expected values, where the non importance sampling approaches severely fail.

<p>Traditionally in human genetic linkage analysis, extended families were only used in the analysis of dichotomous traits, such as Disease/No Disease. For quantitative traits, analyses initially focused on data from family trios (for example, mother, father, and child) or sib-pairs. Recently however, there have been two very important developments in genetics: It became clear that if the disease status of several generations of a family is known and their genetic information is obtained, researchers can pinpoint which pieces of genetic material are linked to the disease or trait. It also became evident that if a trait is quantitative (numerical), as blood pressure or viral loads are, rather than dichotomous, one has much more power for the same sample size. This led to the&nbsp / development of statistical mixed models which could incorporate all the features of the data, including the degree of relationship between each pair of family members. This is necessary because a parent-child pair definitely shares half their genetic material, whereas a pair of cousins share, on average, only an eighth. The statistical methods involved here have however been developed by geneticists, for their specific studies, so there does not seem to be a unified and general description of the theory underlying the methods. The aim of this dissertation is to explain in a unified and statistically comprehensive manner, the theory involved in the analysis of quantitative trait genetic data from extended families. The focus is on linkage analysis: what it is and what it aims to do.&nbsp / There is a step-by-step build up to it, starting with an introduction to genetic epidemiology. This includes an explanation of the relevant genetic terminology. There is also an application section where an appropriate human genetic family dataset is analysed, illustrating the methods explained in the theory sections.</p>

<p>Traditionally in human genetic linkage analysis, extended families were only used in the analysis of dichotomous traits, such as Disease/No Disease. For quantitative traits, analyses initially focused on data from family trios (for example, mother, father, and child) or sib-pairs. Recently however, there have been two very important developments in genetics: It became clear that if the disease status of several generations of a family is known and their genetic information is obtained, researchers can pinpoint which pieces of genetic material are linked to the disease or trait. It also became evident that if a trait is quantitative (numerical), as blood pressure or viral loads are, rather than dichotomous, one has much more power for the same sample size. This led to the&nbsp / development of statistical mixed models which could incorporate all the features of the data, including the degree of relationship between each pair of family members. This is necessary because a parent-child pair definitely shares half their genetic material, whereas a pair of cousins share, on average, only an eighth. The statistical methods involved here have however been developed by geneticists, for their specific studies, so there does not seem to be a unified and general description of the theory underlying the methods. The aim of this dissertation is to explain in a unified and statistically comprehensive manner, the theory involved in the analysis of quantitative trait genetic data from extended families. The focus is on linkage analysis: what it is and what it aims to do.&nbsp / There is a step-by-step build up to it, starting with an introduction to genetic epidemiology. This includes an explanation of the relevant genetic terminology. There is also an application section where an appropriate human genetic family dataset is analysed, illustrating the methods explained in the theory sections.</p>

Magister Scientiae - MSc / Traditionally in human genetic linkage analysis, extended families were only used in the analysis of dichotomous traits, such as Disease/No Disease. For quantitative traits, analyses initially focused on data from family trios (for example, mother, father, and child) or sib-pairs. Recently however, there have been two very important developments in genetics: It became clear that if the disease status of several generations of a family is known and their genetic information is obtained, researchers can pinpoint which pieces of genetic material are linked to the disease or trait. It also became evident that if a trait is quantitative (numerical), as blood pressure or viral loads are, rather than dichotomous, one has much more power for the same sample size. This led to the development of statistical mixed models which could incorporate all the features of the data, including the degree of relationship between each pair of family members. This is necessary because a parent-child pair definitely shares half their genetic material, whereas a pair of cousins share, on average, only an eighth. The statistical methods involved here have however been developed by geneticists, for their specific studies, so there does not seem to be a unified and general description of the theory underlying the methods. The aim of this dissertation is to explain in a unified and statistically comprehensive manner, the theory involved in the analysis of quantitative trait genetic data from extended families. The focus is on linkage analysis: what it is and what it aims to do. There is a step-by-step build up to it, starting with an introduction to genetic epidemiology. This includes an explanation of the relevant genetic terminology. There is also an application section where an appropriate human genetic family dataset is analysed, illustrating the methods explained in the theory sections. / South Africa

Le somnambulisme est un trouble du sommeil fréquent qui appartient à la famille des parasomnies NREM. Malgré des décennies de recherche, sa pathophysiologie reste peu comprise. Les études de familles et les études de jumeaux démontrent qu’une forte composante héréditaire est en jeu. Toutefois, très peu d’études moléculaires ont été menées afin d’identifier des gènes impliqués et il n’y a toujours pas de consensus quant au mode de transmission dans les familles. Cet ouvrage contient deux études distinctes qui tenteront de répondre à ces deux problèmes. L’objectif de la première étude était de déterminer si des variants génétiques dans le gène Adénosine désaminase (ADA) étaient enrichis dans la population somnambule en comparaison avec les dormeurs sains. Le gène entier a été séquencé chez 251 patients somnambules provenant de Montréal et de Montpellier ainsi que chez 94 sujets contrôles sans histoire personnelle ni familiale de somnambulisme. Aucun variant génétique n’était enrichi chez les patients somnambules en comparaison avec les dormeurs sains et les bases de données génétiques publiques. Dans la deuxième étude, le premier objectif était de déterminer le mode de transmission du somnambulisme chez 20 familles canadiennes-françaises. Le deuxième objectif était de mesurer le risque récurrent ainsi que le risque relatif pour la fratrie et les enfants des patients index. Dans notre cohorte, le somnambulisme se transmettait principalement selon un mode autosomal dominant à pénétrance réduite. Les risques récurrents pour les apparentés de premier degré étaient : à vie 0,48 à 0,56, durant l’enfance 0,43 à 0,56 et à l’âge adulte 0,14 à 0,35. Les risques relatifs pour les apparentés de premier degré étaient : à vie 6,96 à 8,12, durant l’enfance 1,48 à 4,06 et à l’âge adulte 4,67 à 11,67 supérieurs à la population générale. D’autres études moléculaires comme le séquençage de l’exome et les études de liaison génétique dans les familles seront nécessaires afin d’identifier de nouveaux gènes candidats qui pourront agir à titre de biomarqueurs. Cela permettrait de faciliter le diagnostic et ultimement développer des approches thérapeutiques ciblées. / Sleepwalking is a common sleep disorder and it belongs to the family of NREM parasomnias. Despite decades of research, its pathophysiology remains poorly understood. Family and twin studies show that a strong hereditary component is involved. However, very few molecular studies have been conducted to identify the genes involved and there is still no consensus on the mode of transmission in families. This Master’s thesis contains two separate studies which will attempt to address these two problems. The aim of the first study was to determine whether genetic variants in the Adenosine Deaminase (ADA) gene were enriched in the sleepwalking population compared to healthy sleepers. The entire gene was sequenced in 251 sleepwalking patients from Montreal and Montpellier as well as in 94 control subjects with no personal or family history of sleepwalking. No genetic variants were enriched in sleepwalking patients compared to healthy sleepers and public genetic databases. In the second study, the first objective was to determine the mode of transmission of sleepwalking in 20 French-Canadian families. The second objective was to measure the recurrence risk as well as the relative risk for siblings and children of index patients. In our cohort, sleepwalking was transmitted mainly in an autosomal dominant mode with reduced penetrance. The recurrence risks for first-degree relatives were: lifetime 0.48 to 0.56, in childhood 0.43 to 0.56, and in adulthood 0.14 to 0.35. The relative risks for first-degree relatives were: lifetime 6.96 to 8.12, in childhood 1.48 to 4.06 and in adulthood 4.67 to 11.67 higher than the general population. Further molecular studies, such as exome sequencing, and genetic linkage studies in families will be needed in order to identify new candidate genes that can act as biomarkers. This would allow the development of an independent test for the diagnosis and ultimately have implications for targeted therapeutic approaches.

No description available.