arXiv subjects

Mauricio A. Elzo

Publications and source records attributed to Mauricio A. Elzo.

5 recordsLinked to original sources

arXiv2017

BIBI: Bayesian Inference of Breed Composition

The aim of this paper was to develop statistical models to estimate individual breed composition based on the previously proposed idea of regressing discrete random variables corresponding to counts of reference alleles of biallelic molecular markers located across the genome on the allele frequencies of each marker in the pure (base) breeds. Some of the existing regression-based methods do not guarantee that estimators of breed composition will lie in the appropriate parameter space and none of them account for uncertainty about allele frequencies in the pure breeds, that is, uncertainty about the design matrix. In order to overcome these limitations, we proposed two Bayesian generalized linear models. For each individual, both models assume that the counts of the reference allele at each marker locus follow independent Binomial distributions, use the logit link, and pose a Dirichlet prior over the vector of regression coefficients (which corresponds to breed composition). This prior guarantees that point estimators of breed composition like the posterior mean pertain to the appropriate space. The difference between these models is that model termed BIBI does not account for uncertainty about the design matrix, while model termed BIBI2 accounts for such an uncertainty by assigning independent Beta priors to the entries of this matrix. We implemented these models in a multibreed Angus-Brahman population. Posterior means were used as point estimators of breed composition. In addition, the ordinary least squares estimator proposed by Kuehn et al. (2011) (OLSK) was also computed. BIBI and BIBI2 estimated breed composition more accurately than OLSK, and BIBI2 had an 8.3% improvement in accuracy as compared to BIBI.

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arXiv2017

Modelling correlated marker effects in genome-wide prediction via Gaussian concentration graph models

In genome-wide prediction, independence of marker allele substitution effects is typically assumed; however, since early stages of this technology it has been known that nature points to correlated effects. In statistics, graphical models have been identified as a useful and powerful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In particular, Gaussian concentration graph models (GCGM) have been widely studied. These are models in which the distribution of a set of random variables, the marker effects in this case, is assumed to be Markov with respect to an undirected graph G. In this paper, Bayesian (Bayes G and Bayes G-D) and frequentist (GML-BLUP) methods adapting the theory of GCGM to genome-wide prediction were developed. Different approaches to define the graph G based on domain-specific knowledge were proposed, and two propositions and a corollary establishing conditions to find decomposable graphs were proven. These methods were implemented in small simulated and real datasets. In our simulations, scenarios where correlations among allelic substitution effects were expected to arise due to various causes were considered, and graphs were defined on the basis of physical marker positions. Results showed improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values when accounting for partially correlated allele substitution effects. Extensions to the multiallelic loci case were described and some possible refinements incorporating more flexible priors in the Bayesian setting were discussed. Our models are promising because they allow incorporation of biological information in the prediction process, and because they are more flexible and general than other models accounting for correlated marker effects that have been proposed previously.

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arXiv2017

Inferring the Partial Correlation Structure of Allelic Effects and Incorporating it in Genome-wide Prediction

In this study, we addressed the problem of genome-wide prediction accounting for partial correlation of marker effects when the partial correlation structure, or equivalently, the pattern of zeros of the precision matrix is unknown. This problem requires estimating the partial correlation structure of marker effects, that is, learning the pattern of zeros of the corresponding precision matrix, estimating its non-null entries, and incorporating the inferred concentration matrix in the prediction of marker allelic effects. To this end, we developed a set of statistical methods based on Gaussian concentration graph models (GCGM) and Gaussian directed acyclic graph models (GDAGM) that adapt the existing theory to perform covariance model selection (GCGM) or DAG selection (GDAGM) to genome-wide prediction. Bayesian and frequentist approaches were formulated. Our frequentist formulations combined some existing methods with the EM algorithm and were termed Glasso-EM, CONCORD-EM and CSCS-EM, whereas our Bayesian formulations corresponded to hierarchical models termed Bayes G-Sel and Bayes DAG-Sel. Results from a simulation study showed that our methods can accurately recover the partial correlation structure and estimate the precision matrix. Methods CONCORD-EM and Bayes G-Sel had an outstanding performance in estimating the partial correlation structure and a method based on CONCORD-EM yielded the most accurate estimates of the precision matrix. Our methods can be used as predictive machines and as tools to learn about the covariation of effects of pairs of loci on a given phenotype conditioned on the effects of all the other loci considered in the model. Therefore, they are useful tools to learn about the underlying biology of a given trait because they help to understand relationships between different regions of the genome in terms of the partial correlations of their effects on that trait.

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arXiv2017

Introducing Gaussian covariance graph models in genome-wide prediction

Several statistical models used in genome-wide prediction assume independence of marker allele substitution effects, but it is known that these effects might be correlated. In statistics, graphical models have been identified as a useful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In Gaussian covariance graph models (GCovGM), the joint distribution of a set of random variables is assumed to be Gaussian and the pattern of zeros of the covariance matrix is encoded in terms of an undirected graph G. In this study, methods adapting the theory of GCovGM to genome-wide prediction were developed (Bayes GCov, Bayes GCov-KR and Bayes GCov-H). In simulated and real datasets, improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values were found. Our models account for correlation of marker effects and permit to accommodate general structures as opposed to models proposed in previous studies which consider spatial correlation only. In addition, they allow incorporation of biological information in the prediction process through its use when constructing graph G, and their extension to the multiallelic loci case is straightforward.

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arXiv2015

On the Bayesness, minimaxity, and admissibility of point estimators of allelic frequencies

In this paper, decision theory was used to derive Bayes and minimax decision rules to estimate allelic frequencies and to explore their admissibility. Decision rules with uniformly smallest risk usually do not exist and one approach to solve this problem is to use the Bayes principle and the minimax principle to find decision rules satisfying some general optimality criterion based on their risk functions. Two cases were considered, the simpler case of biallelic loci and the more complex case of multiallelic loci. For each locus, the sampling model was a multinomial distribution and the prior was a Beta (biallelic case) or a Dirichlet (multiallelic case) distribution. Three loss functions were considered: squared error loss (SEL), Kulback-Leibler loss (KLL) and quadratic error loss (QEL). Bayes estimators were derived under these three loss functions and were subsequently used to find minimax estimators using results from decision theory. The Bayes estimators obtained from SEL and KLL turned out to be the same. Under certain conditions, the Bayes estimator derived from QEL led to an admissible minimax estimator (which was also equal to the maximum likelihood estimator). The SEL also allowed finding admissible minimax estimators. Some estimators had uniformly smaller variance than the MLE and under suitable conditions the remaining estimators also satisfied this property. In addition to their statistical properties, the estimators derived here allow variation in allelic frequencies, which is closer to the reality of finite populations exposed to evolutionary forces.

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