SeqBreed : a python tool to evaluate genomic prediction in complex scenarios

Background: Genomic prediction (GP) is a method whereby DNA polymorphism information is used to predict breeding values for complex traits. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help design optimum breeding programs and experiments, including genome-wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible forward simulator programmed in python3. Results: SeqBreed accommodates sex and mitochondrion chromosomes as well as autopolyploidy. It can simulate any number of complex phenotypes that are determined by any number of causal loci. SeqBreed implements several GP methods, including genomic best linear unbiased prediction (GBLUP), single-step GBLUP, pedigree-based BLUP, and mass selection. We illustrate its functionality with Drosophila genome reference panel (DGRP) sequence data and with tetraploid potato genotype data. Conclusions: SeqBreed is a flexible and easy to use tool that can be used to optimize GP or genome-wide association studies. It incorporates some of the most popular GP methods and includes several visualization tools. Code is open and can be freely modified. Software, documentation, and examples are available at https://github.com/miguelperezenciso/SeqBreed

Conceptual hydrological model calibration using multi-objective optimization techniques over the transboundary Komadugu-Yobe basin, Lake Chad Area, West Africa

Study Area: The discharge of the transboundary Komadugu-Yobe Basin, Lake Chad Area, West Africa is calibrated using multi-objective optimization techniques. Study focus: The GR5J hydrological model parameters are calibrated using six optimization methods i.e. Local Optimization-Multi Start (LOMS), the Differential Evolution (DE), the Multiobjective Particle the Swarm Optimization (MPSO), the Memetic Algorithm with Local Search Chains (MALS), the Shuffled Complex Evolution-Rosenbrock’s function (SCE-R), and the Bayesian Markov Chain Monte Carlo (MCMC) approach. Three combined objective functions i.e. Root Mean Square Error, Nash- Sutcliffe efficiency, Kling-Gupta efficiency are applied. The calibration process is divided into two separate episodes (1974–2000 and 1980–1995) so as to ascertain the robustness of the calibration approaches. Runoff simulation results are analysed with a timefrequency wavelet transform. New hydrological insights for the region: For calibration and validation stages, all optimization methods simulate the base flow and high flow spells with a satisfactory level of accuracy. For calibration period, MCMC underestimate it by -0.07 mm/day. The performance evaluation shows that MCMC has the highest values of mean absolute error (0.28) and mean square error (0.40) while LOMS and MCMC record a low volumetric efficiency of 0.56. In all cases, the DE and the SCE-R methods perform better than others. The combination of multi-objective functions and multi-optimization techniques improve the model’s parameters stability and the algorithms’ optimization to represent the runoff in the basin

An approximate theory of selection assuming a finite number of quantitative trait loci

An approximate theory of mid-term selection for a quantitative trait is developed for the case when a finite number of unlinked loci contribute to phenotypes. Assuming Gaussian distributions of phenotypic and genetic effects, the analysis shows that the dynamics of the response to selection is defined by one single additional parameter, the effective number Le of quantitative trait loci (QTL). This number is expected to be rather small (3-20) if QTLs have variable contributions to the genetic variance. As is confirmed by simulation, the change with time of the genetic variance and of the cumulative response to selection depend on this effective number of QTLs rather than on the total number of contributing loci. The model extends the analysis of Bulmer, and shows that an equilibrium structure arises after a few generations in which some amount of genetic variability is hidden by gametic disequilibria. The additive genetic variance VA and the genic variance Va remain linked by: (formula, see attached document) where K is the proportion of variance removed by selection, and h2 the current heritability of the trait. From this property, a complete approximate theory of selection can be developed, and modifications of correlations between relatives can be proposed. However, the model generally overestimates the cumulative response to selection except in early generations, which defines the time scale for which the present theory is of potential practical value.Une théorie approchée de la sélection est développée dans le cas d’un caractère quantitatif dont la variabilité génétique est due à un nombre fini de locus génétiquement indépendants. Le calcul est développé analytiquement en admettant que toutes les distributions statistiques peuvent être approchées par des lois normales. L’analyse montre que le comportement global du système génétique dépend essentiellement d’un «nombre efficace de locus», Le, dont les valeurs vraisemblables sont sans doute faibles (3 à 20). Des simulations confirment le rôle de ce paramètre pour caractériser la réponse cumulée à la sélection et la structure génétique de la population. Le modèle généralise l’analyse de M Bulmer. Après quelques générations d’un régime de sélection, une fraction de la variance génétique reste « cachée» sous la forme de covariances négatives, de sorte que la variance génétique additive VA et la variance génique Va demeurent liées par la relation :( formule, voir document attaché) où k est la fraction de variance réduite par la sélection, et h2 est l’héritabilité actuelle du caractère. Cette structuration de la variance génétique sous sélection permet de proposer des expressions modifiées des covariances entre apparentes issus de parents sélectionnés, et de développer une théorie complète de la sélection. Sauf à court et moyen terme, les prédictions quantitatives sont surestimées par le modèle gaussien, ce qui délimite le champ d’application pratique de la théorie

Intelligent simulation of coastal ecosystems

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto, Faculdade de Ciência e Tecnologia. Universidade Fernando Pessoa. 201

Effect of including major gene information in mass selection: a stochastic simulation in a small population

Using a system of recurrence equations, best linear unbiased prediction applied to a reduced animal model (RAM) is presented for marker-assisted selection. This approach is a RAM version of the method with the animal model to reduce the number of equations per animal to one. The current RAM approach allows simultaneous evaluation of fixed effects and total additive genetic merit which is expressed as the sum of the additive genetic effects due to quantitative trait loci (QTL) unlinked to the marker locus (ML) and the additive effects due to the QTL linked to the ML. The total additive genetic merits for animals with no progeny are predicted by the formulae derived for backsolving. A numerical example is given to illustrate the current RAM approach.Sur la base d’un système d’équations de récurrence, la méthode du meilleur prédicteur linéaire sans biais appliquée à un modèle animal réduit (MAR) est présentée pour la sélection assistée par marqueur. Cette méthode est une version MAR de celle du modèle animal pour réduire à un le nombre d’équations par animal. Cette méthode MAR permet d’estimer simultanément les effets fixés et la valeur génétique globale, qui est la somme des effets génétiques additifs des locus de caractère quantitatif (QTL) non liés au locus marqueur et des effets additifs des QTL liés au locus marqueur. La valeur génétique globale des animaux sans descendance est prédite par un système d’équations reconstitué à partir du système principal. Un exemple numérique est donné pour illustrer la méthode MAR présentée ici

Estimating hybridization in the presence of coalescence using phylogenetic intraspecific sampling

Abstract Background A well-known characteristic of multi-locus data is that each locus has its own phylogenetic history which may differ substantially from the overall phylogenetic history of the species. Although the possibility that this arises through incomplete lineage sorting is often incorporated in models for the species-level phylogeny, it is much less common for hybridization to also be formally included in such models. Results We have modified the evolutionary model of Meng and Kubatko (2009) to incorporate intraspecific sampling of multiple individuals for estimation of speciation times and times of hybridization events for testing for hybridization in the presence of incomplete lineage sorting. We have also utilized a more efficient algorithm for obtaining our estimates. Using simulations, we demonstrate that our approach performs well under conditions motivated by an empirical data set for Sistrurus rattlesnakes where putative hybridization has occurred. We further demonstrate that the method is able to accurately detect the signature of hybridization in the data, while this signal may be obscured when other species-tree inference methods that ignore hybridization are used. Conclusions Our approach is shown to be powerful in detecting hybridization when it is present. When applied to the Sistrurus data, we find no evidence of hybridization; instead, it appears that putative hybrid snakes in Missouri are most likely pure S. catenatus tergeminus in origin, which has significant conservation implications.</p