Likelihood analysis of population genetic data under coalescent models: computational and inferential aspects

Abstract

Likelihood methods are being developed for inference of migration rates and past demographic changes from population genetic data. We survey an approach for such inference using sequential importance sampling techniques derived from coalescent and diffusion theory. The consistent application and assessment of this approach has required the re-implementation of methods often considered in the context of computer experiments methods, in particular of Kriging which is used as a smoothing technique to infer a likelihood surface from likelihoods estimated in various parameter points, as well as reconsideration of methods for sampling the parameter space appropriately for such inference. We illustrate the performance and application of the whole tool chain on simulated and actual data, and highlight desirable developments in terms of data types and biological scenarios.\n\nResumeDiverses approches ont ete developpees pour linference des taux de migration et des changements demo-graphiques passes a partir de la variation genetique des populations. Nous decrivons une de ces approches utilisant des techniques dechantillonnage pondere sequentiel, fondees sur la modelisation par approches de coalescence et de diffusion de levolution de ces polymorphismes. Lapplication et levaluation systematique de cette approche ont requis la re-implementation de methodes souvent considerees pour lanalyse de fonctions simulees, en particulier le krigeage, ici utilise pour inferer une surface de vraisemblance a partir de vraisemblances estimees en differents points de lespace des parametres, ainsi que des techniques dechantillonage de ces points. Nous illustrons la performance et lapplication de cette serie de methodes sur donnees simulees et reelles, et indiquons les ameliorations souhaitables en termes de types de donnees et de scenarios biologiques.\n\nMots-cleshistoire demographique, processus de coalescence, importance sampling, genetic polymorphism\n\nAMS 2000 subject classifications92D10, 62M05, 65C05