Genealogical data of Boer and Nubian goats in Mexico

The pedigree file of the Boer and Nubian goat breeds in Mexico was constructed using the national database provided by the Asociación Mexicana de Criadores de Ganado Caprino de Registro. Field technicians routinely updated the goat national database by recording information from flocks participating in the performance-recording system. Information on animal identification number, parents, birth date, sex, breed, and farm of origin were used to undertake pedigree analyses using the ENDOG program (version 4.8). This paper presents a pedigree data file, tables and figures of characteristics of pedigree data, pedigree analyses, pedigree integrity, effective population size and genetic conservation index. The data can be used to estimate other population parameters, to monitor the genetic diversity of the Boer and Nubian goat breeds in Mexico, and also to design balanced breeding programs, maintaining genetic variation at reasonable levels and maximizing genetic progress in these populations.202

An analysis of the 1948 Jersey heifer registration data to provide information concerning some features of New Zealand pedigree Jersey cattle

At present there is a lack of information concerning the breeding methods practised by owners of pedigree dairy cattle in New Zealand. This deficiency is a serious handicap in attacking some of the major problems facing the dairy industry in this country. Since the emergence of the Dairy Board in 1936 as the main national force concerned with herd improvement a great deal has been achieved in the field of dairy cattle improvement and the evolution of a sire survey applicable to conditions obtaining in New Zealand is one of the major achievements of the Dairy Board. But many aspects of the pedigree industry which provides the majority of sires used in the Dominion remain to be elucidated. For example, the present scarcity of proven sires, and the more or less stationary level of production of commercial herds are some of the problems which have direct bearing on the pedigree section of the dairy industry. These facts alone illustrate the importance of the study of current breeding methods and of the pedigree system as it exists in New Zealand. Many investigations relating to dairy cattle breeding have been carried out overseas. Although many of the problems studied are encountered in New Zealand, the Dominion's typical grassland farming and the seasonal dairying associated with it limit the usefulness of such investigations. With more information becoming available breed societies are beginning to adjust themselves to a new phase of cattle breeding. The increasing emphasis on production as an aid to selection, and the acceptance of artificial insemination are two phases in which a change of attitude is desirable. The rapid growth of pedigree herds, the more frequent appearance of performance details in pedigrees and the increasing number of pedigree animals mated artificially each year illustrate this gradual change

Reviewing the Drivers and Challenges in RFID Implementation in the Pharmaceutical Supply Chain

Counterfeiting is a global phenomenon that poses a serious financial threat to the pharmaceutical industry and more importantly jeopardizes public safety and security. Different measures, including new laws and regulations, have been put in place to mitigate the threat and tighten control in the pharmaceuticals supply chain. However, it appears that the most promising countermeasure is track-and-trace technology such as electronic-pedigree (E-pedigree) with Radio Frequency Identification (RFID) technology. In this study we present a framework exploring the antecedents and consequences of RFID applications in the pharmaceutical supply chain. The framework proposes that counterfeiting and E-pedigree regulation will drive the implementation of RFID in the pharmaceutical supply chain, which in turn provides strategic and operational benefits that enable competitive advantage. Meanwhile, the implementation of RFID requires overcoming many operational, technical and financial challenges. The framework provides a springboard that future study can explore using empirical data

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even data sets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper we re-examine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (6 CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1357 individuals in 124 pedigrees) takes less than 2 minutes and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 minutes and 1.5 GB of memory