Ratio between inbreeding and coancestry rates as a measure of population subdivision. Preliminary results

Ponencia publicada en ITEA, vol. 104Uno de los factores que provocan incrementos de consanguinidad superiores a los que se esperarían en función del tamaño de la población es la subdivisión de las poblaciones. La medida de la subdivisión de poblaciones no resulta sencilla. Se presenta una medida de subdivisión de poblaciones que se obtiene a partir de la información de pedigree. Se basa en la comparación directa de los incrementos de coascendencia en relación a los incrementos de endogamia por generación discreta equivalente, establecidos ambos a partir de la media de valores individuales de esos parámetros. La utilidad del parámetro desde el punto de vista descriptivo se ilustra con la ayuda de una población simulada y de dos poblaciones reales con diferentes escenarios de subdivisión.Subdivision is one of the factors leading to increase in inbreeding higher than those expected regarding population size. Measuring population subdivision is not straightforward given that it is established rather diffuse. A measure of population subdivision from pedigrees is presented. It is based on the direct comparison of the increases in coancestry and the increases in inbreeding computed over equivalent discrete generation. Coancestry and inbreeding rates were established from the average of individual values of those parameters. The usefulness of the new parameter from a descriptive point of view is illustrated using a simulated population and two real populations with an opposed scenario regarding subdivision

Herdbook analyses of the Asturiana beef cattle breeds

International audienc

Learning data structure from classes: A case study applied to population genetics

In most cases, the main goal of machine learning and data mining applications is to obtain good classifiers. However, final users, for instance researchers in other fields, sometimes prefer to infer new knowledge about their domain that may be useful to confirm or reject their hypotheses. This paper presents a learning method that works along these lines, in addition to reporting three interesting applications in the field of population genetics in which the aim is to discover relationships between species or breeds according to their genotypes. The proposed method has two steps: first it builds a hierarchical clustering of the set of classes and then a hierarchical classifier is learned. Both models can be analyzed by experts to extract useful information about their domain. In addition, we propose a new method for learning the hierarchical classifier. By means of a voting scheme employing pairwise binary models constrained by the hierarchical structure, the proposed classifier is computationally more efficient than previous approaches while improving on their performance