Likelihood inferences in animal breeding under selection: a missing-data theory view point

Data available in animal breeding are often subject to selection. Such data can be viewed as data with missing values. In this paper, inferences based on likelihoods derived from statistical models for missing data are applied to production records subject to selection. Conditions for ignoring the selection process are discussed.Les données disponibles en génétique animale sont souvent issues d’un processus préalable de sélection. On peut donc considérer comme manquants les attributs (non observés) associés aux individus éliminés, et analyser les données recueillies comme provenant d’un échantillon avec données manquantes. Dans cet article,on développe les méthodes d’inférence fondées sur les vraisemblances, en explicitant dans leur calcul le processus, dû à la sélection, qui induit les données manquantes. On discute les conditions dans lesquelles on peut ignorer la sélection, et donc considérer seulement la vraisemblance des données effectivement recueillies

Empirical Bayes estimation of parameters for n polygenic binary traits

The conditional probability of an observation in a subpopulation i (a combination of levels of explanatory variables) falling into one of 2n mutually exclusive and exhaustive categories is modelled using a normal integral in n-dimensions. The mean of subpopulation i is written as a linear combination of an unknown vector θ which can include « fixed » effects (e.g., nuisance environmental effects, genetic group effects) and « random » effects such as additive genetic value or producing ability. Conditionally on θ, the normal integral depends on an unknown matrix R comprising residual correlations in a multivariate standard normal conceptual scale. The random variables in θ have a dispersion matrix G X A, where usually A is a known matrix of additive genetic relationships, and G is a matrix of unknown genetic variances and covariances. It is assumed a priori that θ follows a multivariate normal distribution f (θ | G), which does not depend on R, and the likelihood function is taken as product multinomial. The point estimator of θ is the mode of the posterior distribution f (θ | Y, G = G*, R = R*) where Y is data, and G* and R* are the components of the mode of the marginal posterior distribution f (G, R | Y) using « flat » priors for G and R. The matrices G* and R* correspond to the marginal maximum likelihood estimators of the corresponding matrices. The point estimator of θ is of the empirical Bayes types. Overall, computations involve solving 3 non-linear systems in θ, G and R. G* can be computed with an expectation-maximization type algorithm ; an estimator of R* is suggested, and this is related to results published elsewhere on maximum likelihood estimation in contingency tables. Problems discussed include non-linearity, size of the system to be solved, rate of convergence, approximations made and the possible use of informative priors for the dispersion parameters.La probabilité conditionnelle qu’une observation d’une sous-population donnée (combinaison de niveaux de facteurs) se trouve dans l’une des 2" catégories possibles de réponse (exclusives et exhaustives) est modélisée par une intégrale normale à n-dimensions. La moyenne de la ﺎe sous population s’écrit comme une combinaison linéaire d’un vecteur θ de paramètres inconnus qui peuvent comprendre des effets « fixes » (effets de milieu parasites, effets de groupe génétique) et des effets aléatoires (valeur génétique additive ou aptitude à la production). Sachant θ, l’intégrale normale dépend d’une matrice inconnue R fonction des corrélations résiduelles entre les n variables normales sous-jacentes standardisées. Les effets aléatoires de θ présentent une matrice de dispersion de la forme G X A où A est généralement une matrice connue de parenté et G une matrice inconnue de variances et covariances génétiques. On suppose qu’a priori θ suit une loi multinormale de densité f (θ | G) qui ne dépend pas de R. La vraisemblance s’exprime alors comme un produit de multinomiales. L’estimateur de position de θ est défini comme le mode de la distribution a posteriori f (θ | Y, G = G*, R = R*) où Y est le vecteur des données, G* et R* sont les composantes du mode de la distribution marginale f (G, R | Y) avec des a priori uniformes pour G et R. G* et R* correspondent alors aux estimateurs du maximum de vraisemblance marginale et θ à un estimateur de type bayésien empirique. Les calculs impliquent la résolution de 3 systèmes non-linéaires en θ, G et R. G* se calcule selon un algorithme de type E.M. Une approximation de R* est suggérée en relation avec des résultats antérieurs publiés à propos d’une estimation du maximum de vraisemblance pour les tables de contingence. Divers problèmes sont abordés en discussion tels que la non-linéarité, la taille du système à résoudre, la vitesse de convergence, le degré d’approximation et l’emploi possible d’a priori informatifs pour les paramètres de dispersion

Bayesian inference in threshold models using Gibbs sampling

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Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle

open6siopenDadousis, C.; Pegolo, S.; Rosa, G.J.M.; Gianola, D.; Bittante, G.; Cecchinato, ADadousis, Christos; Pegolo, Sara; Rosa, G. J. M.; Gianola, D.; Bittante, Giovanni; Cecchinato, Alessi

Ecriture des équations du BLUP multicaractères

Cet article formalise l’écriture des équations du BLUP multicaractères dans la situation où tous les caractères sont affectés par les mêmes facteurs de variation. Deux cas sont distingués selon que l’information sur les caractères est complète ou non. En présence d’un seul facteur aléatoire et quand tous les caractères sont contrôlés, le problème se simplifie beaucoup grâce à une transformation canonique des données (ou directement des seconds-membres) et revient à calculer le BLUP unicaractère de chacune des variables canoniques. Dans le cas d’information incomplète, on est conduit à subdiviser l’échantillon en ensembles d’individus homogènes entre eux quant aux caractères contrôlés et tels que les résiduelles du modèle relatives aux variables mesurées sur ces individus soient non corrélées entre elles d’un ensemble à l’autre. Le système obtenu présente un nombre d’équations multiple du nombre de caractères ce qui pose des problèmes numériques importants et limite beaucoup la taille des applications actuelles. Cependant dans tous les cas, il est possible d’obtenir très simplement les BLUP d’individus apparentés et de caractères corrélés n’apparaissant pas dans la décomposition des données. Un exemple très simple est présenté en détail pour illustrer le raisonnement.This paper gives expressions for multiple trait BLUP equations when the same factors are influencing all traits. Two cases are discussed depending on whether the information on the traits is complete or not. When all traits are recorded and with only one random factor considered in the model, the problem is simplified because a canonical transformation can be applied to the data or to the right-band sides of the mixed model equations. The system reduces to single trait BLUP evaluations. When records on some ttaits are missing, the sample can be divided into disjoint subsets of individuals having the same traits recorded within a group so that residuals are uncorrelated among subsets. In the system so obtained, the number of equations is a multiple of the number of traits leading to severe numerical difficulties, thus limiting the size of present applications. Nevertheless, it is easy in every case to get predictions of related individuals and correlated traits not described in the model for the data. A simple example is presented to illustrate the formulae

How good are low back pain guidelines? A critical appraisal of the quality of clinical practice guidelines using the agree II tool

Clinical practice guidelines (CPGs) provide evidence-based recommendations for clinical practice, but their increasing number in the last few years arises possible concerns about their quality. Preliminary results on the methodological quality of CPGs for low back pain management (LBP) are here presented. The results of this review can help researchers and Italian policymakers select and adopt the highest quality Clinical Practice Guidelines (CPGs) for Low Back Pain (LBP) management in the CPG National Systems (Sistema Nazionale Linee Guida)

Effectiveness and safety of virtual reality rehabilitation after stroke: an overview of systematic reviews

Background: Virtual reality (VR) is an innovative neurorehabilitation modality that has been variously examined in systematic reviews. We assessed VR effectiveness and safety after cerebral stroke. Methods: In this overview of systematic reviews, we searched eleven databases (Cochrane Database of Systematic Reviews, EMBASE, MEDLINE, SCOPUS, ISI Web of Science, CINAHL, PsycINFO, Pedro, Otseeker, Healthevidence.org, Epistemonikos) and grey literature from inception to January 17, 2023. Studies eligible for inclusion were systematic reviews published in English that included adult patients with a clinical diagnosis of stroke (acute to chronic phase) undergoing any kind of immersive, semi-immersive or non-immersive VR intervention with or without conventional therapy versus conventional therapy alone. The primary outcome was motor upper limb function and activity. The secondary outcomes were gait and balance, cognitive and mental function, limitation of activities, participation, and adverse events. We calculated the degree of overlap between reviews based on the corrected covered area (CCA). Methodological quality was assessed using the A MeaSurement Tool to Assess systematic Reviews (AMSTAR 2) and the Certainty of Evidence (CoE) using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Discordances between results were examined using a conceptual framework based on the Jadad algorithm. This overview is registered with PROSPERO, CRD42022329263. Findings: Of the 58 reviews included (n = 345 unique primary studies), 42 (72.4%) had conducted meta-analysis. More than half of the reviews (58.6%) were published between 2020 and 2022 and many (77.6%) were judged critically low in quality by AMSTAR 2. Most reported the Fugl Meyer Assessment scale (FMA-UE) to measure upper limb function and activity. For the primary outcome, there was a moderate overlap of primary studies (CCA 9.0%) with discordant findings. Focusing on upper limb function (FMA-UE), VR with or without conventional therapy seems to be more effective than conventional therapy alone, with low to moderate CoE and probable to definite clinical relevance. For secondary outcomes there was uncertainty about the superiority or no difference between groups due to substantial heterogeneity of measurement scales (eg, methodological choices). A few reviews (n = 6) reported the occurrence of mild adverse events. Interpretation: Current evidence suggests that multiple meta-analyses agreed on the superiority of VR with or without conventional therapy over conventional therapy on FME-UE for upper limb. Clinicians may consider embedding VR technologies into their practice as appropriate with patient's goals, abilities, and preferences. However, caution is needed given the poor methodological quality of reviews. Funding: Italian Ministry of Health