This study reports the distribution of sperm morphology defects by breed, age, season and region of 11,387 bulls in 500 herds in Australia and near Pacific Islands during annual BBSE. Bull location was divided into 4 broad climatic regions based upon temperature, vegetation and climatic risk. Taking into account the impact of age, season, region, and breed there were differences between breeds in both percent morphologically normal sperm and in some individual categories of sperm abnormality (P < 0.001). Independent of breed, season and region, proximal droplets were significantly increased in bulls less than 20 months of age. This is the first study to comprehensively collect data from this wide geographical area and compare sperm morphology profiles among the Bos indicus and Bos taurus breeds. The findings of this study will act as a guide for veterinary practitioners and cattle breeders in the proportion of bulls that can be expected to pass the PNS test, by breed, age and region, based on a robust data set.
Learning from the Bayesian perspective can be described simply as the modification of opinion based on experience. This is in contrast to the Classical or “frequentist” approach that begins with no prior opinion, and inferences are based strictly on information obtained from a random sample selected from the population. An Internet search will quickly provide evidence of the growing popularity of Bayesian methods for data mining in a plethora of subject areas, from agriculture to genetics, engineering, and finance, to name a few. However, despite acknowledged advantages of the Bayesian approach, it is not yet routinely used as a tool for knowledge development. This is, in part, due to a lack of awareness of the language, mechanisms and interpretation inherent in Bayesian modeling, particularly for those trained under a foreign paradigm. The aim of this chapter is to provide a gentle introduction to the topic from the KDD perspective. The concepts involved in Bayes’ Theorem are introduced and reinforced through the application of the Bayesian framework to three traditional statistical and/or machine learning examples: a simple probability experiment involving coin tossing, Bayesian linear regression and Bayesian neural network learning. Some of the problems associated with the practical aspects of the implementation of Bayesian learning are then detailed, and various software freely available on the Internet is introduced. The advantages of the Bayesian approach to learning and inference, its impact on diverse scientific fields and its present applications are identified.
Abstract Objective —To determine prevalence, level of inbreeding, heritability, and mode of inheritance for rupture of the cranial cruciate ligament (RCCL) in Newfoundlands. Design —Retrospective and recruitment study. Animals —574 client-owned Newfoundlands. Procedure —Medical records from January 1, 1996, to December 31, 2002, were evaluated for prevalence of RCCL. A pedigree was constructed by use of recruited Newfoundlands with RCCL status based on results of veterinary examination; level of inbreeding, heritability, and mode of inheritance were calculated. Results —Hospital prevalence for RCCL was 22%; dogs in the pedigree from the recruitment study had a mean level of inbreeding of 1.19 × 10 −4 , heritability of 0.27, and a possible recessive mode of inheritance with 51% penetrance for RCCL. Conclusions and Clinical Relevance —Identification of a genetic basis for RCCL in Newfoundlands provided evidence that investigators can now focus on developing methods to identify carriers to reduce the prevalence of RCCL.
Summary This paper investigates the potential problems associated with assuming incorrect population frequencies for segregation analysis when animals are genotyped one by one in a cyclic fashion with segregation analysis carried out at each cycle. The base population allele frequencies of 0.1 and 0.5 studied, with the incorrect frequencies assumed for segregation analysis of 0.5 and 0.1 respectively, were investigated on the basis of their covering the range of possible frequencies and the most erroneous assumption of frequencies. An index modelled using linear regression (LR) was employed to choose the next animal to be genotyped in each cycle, based on segregation analysis at the incorrect frequency. The resultant utility was evaluated both at the correct frequency giving actual utility and at the incorrect frequency, the perceived utility. The results are compared with those in which all segregation analysis was carried out at the correct base population allele frequency. The assumption of incorrect population frequency for segregation analysis leads to a decline in the predictive performances of both indices in terms of the true utility of prediction. When utility is also computed at the incorrect frequency to give perceived utility, the assumption of incorrect population frequency of q = 0.5 for segregation analysis of populations simulated at q = 0.1 leads to a perceived decline in the predictive performance of the LR index. However, the assumption of incorrect population frequency of q = 0.1 for segregation analysis of populations simulated at q = 0.5 leads to a perceived increased utility. This latter paradox is explained in terms of genotype probabilities with reference to the method of construction of the genotype probability index.
Learning from the Bayesian perspective can be described simply as the modification of opinion based on experience. This is in contrast to the Classical or "frequentist" approach that begins with no prior opinion, and inferences are based strictly on information obtained from a random sample selected from the population. An Internet search will quickly provide evidence of the growing popularity of Bayesian methods for data mining in a plethora of subject areas, from agriculture to genetics, engineering, and finance, to name a few. However, despite acknowledged advantages of the Bayesian approach, it is not yet routinely used as a tool for knowledge development. This is, in part, due to a lack of awareness of the language, mechanisms and interpretation inherent in Bayesian modeling, particularly for those trained under a foreign paradigm. The aim of this chapter is to provide a gentle introduction to the topic from the KDD perspective. The concepts involved in Bayes' Theorem are introduced and reinforced through the application of the Bayesian framework to three traditional statistical and/or machine learning examples: a simple probability experiment involving coin tossing, Bayesian linear regression and Bayesian neural network learning. Some of the problems associated with the practical aspects of the implementation of Bayesian learning are then detailed, and various software freely available on the Internet is introduced. The advantages of the Bayesian approach to learning and inference, its impact on diverse scientific fields and its present applications are identified.Request access from your librarian to read this chapter's full text.
Summary This research compares three different indices for ranking animals for genotyping for a single biallelic locus. The indices were designed to predict which animal to genotype in each genotyping cycle in order to maximize the utility of the resulting information across the entire population. In each genotyping cycle, a single animal is genotyped, segregation analysis used to provide genotype probabilities for the loci of ungenotyped animals, and the index applied to determine which animal to genotype in the next cycle. The first index is based on a linear regression model combining seven herd and individual animal attributes, with the remaining two indices based on genotype probability index (GPI) and numerator relationship (CON). The linear model gives superior predictive performance, in terms of utility (herd average GPI) for the initial 5% of the population genotyped. After this point, the best index is (CON−GPI), which combines a high degree of relationship of the animal to all other live animals in the pedigree, with a low GPI.
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