Disruption in the Supply Chain for Beef and Pork: What Has Happened and What Was NAFTA Doing

Industrial Organization, International Relations/Trade,

Optimal design for the detection of a major gene segregation in crosses between 2 pure lines

A simulation method was used to compare different experimental designs for their power to detect a major gene using a maximum likelihood approach. The optimal design is most often the production of F2 as the only segregating genetic type, with a limited effect of the relative numbers of F2s and non-segregating groups (parentals and F1) on the power. Dominant genes were more easily detected than additive ones. A model dealing with the heteroskedasticity of the polygenic component was also studied

Comparison of four statistical methods for detection of a major gene in a progeny test design

In livestock improvement it is common to design a progeny test of sires in order to estimate their breeding values. The data recorded for these estimate are useful for the detection of major genes. They are the n.m performances Yij of m progeny j of n sires i. These data need to be corrected for the polygenic influence of the sire on its progeny (sire i effect Ui). Four statistical tests of the segregation of a major gene are compared. The first (ﺎSA for "segregation analysis") is the classical ratio of the likelihoods under Ho (no major gene) and H1 (a major gene is segregating). The parameters describing the population (means and standard deviations within genotype) are estimated by maximizing the marginal likelihood of the Yij. The other statistics studied are approximations of this ﺎSA statistic where the sire i effect (Ui) is considered as a fixed effect (ﺎ FE statistic) or, following Elsen et al. (1988) and Höschele (1988), where the parameters, and Ui, are estimated maximizing the joint likelihood of Ui and Yij (ﺎME and ﺎME2 statistics). Simulation studies were done in order to describe the distribution of these statistics. It is shown that ﺎSA and ﺎME1 are the most powerful test, followed by ﺎME2 whose relative loss of power ranged between 20 and 40%, depending on the H1 case studied, when 400 progeny are measured (n = m = 20). The segregation analysis, based on direct maximization of the likelihood, required 30 times more computation time than the ﺎME test using an EM algorithm.Il est fréquent, en sélection, de tester sur descendance, des mâles, afin d’estimer leur valeur génétique. Les données recueillies dans ce but peuvent être utilisées afin de mettre en évidence un gène majeur. Elles sont constituées des n.m performances Yij de m descendants j de n mâles i. Ces données doivent être corrigées pour l’effet polygénique du père (Ui) sur ses descendants. Quatre tests statistiques de mise en évidence d’un tel gène majeur sont comparés. Le premier (ﺎSA pour "segregation analysis") est le rapport classique des vraisemblances sous Ho (pas de gène majeur) et sous H1 (existence d’un gène majeur). Les paramètres caractéristiques de la population (moyennes et écarts types intragénotype) sont estimés en maximisant la vraisemblance marginale des Yij Les autres statistiques de tests sont des approximations de ﺎSA pour lesquelles, soit l’effet père Ui est considéré comme un effet fixé (test IFE) soit, comme proposé par Elsen et al. (1988) et Höschele (1988), les paramètres, et Ui, sont obtenus en maximisant la vraisemblance conjointe des Yij et des Ui (test ﺎME1 et ﺎME2 Nous avons réalisé des simulations afin de décrire les distributions de ces tests. ﺎSA et ﺎME1 sont les tests les plus puissants, suivi par ﺎME2 dont la perte relative de puissance varie entre 20 et 40% selon l’hypothèse H1 étudiées, quand 400 descendants sont mesurés (n = m =20). L’analyse de ségrégation, réalisée par maximisation directe de la vraisemblance, demande 30 fois plus de temps de calcul que les tests ﺎME réalisés l’aide d’un algorithme EM

Metabolomics of fecal samples: a practical consideration

Background Metabolic profiling is becoming increasingly popular to identify subtle metabolic variations induced by diet alterations and to characterize the metabolic impact of variations of the gut microbiota. In this context, fecal samples, that contain unabsorbed metabolites, offer a direct access to the outcome of diet - gut microbiota metabolic interactions. Hence, they are a useful addition to measure the ensemble of endogenous and microbial metabolites, also referred to as the hyperbolome. Scope and Approach Many reviews have focused on the metabolomics analysis of urine, plasma and tissue biopsies; yet the analysis of fecal samples presents some challenges that have received little attention. We propose here a short review of current practices and some practical considerations when analyzing fecal material using metabolic profiling of small polar molecules and lipidomics. Key Findings and Conclusions: To allow for a complete coverage of the fecal metabolome, it is recommended to use a combination of analytical techniques that will measure both hydrophilic and hydrophobic metabolites. A clear set of guidelines to collect, prepare and analyse fecal material is urgently needed