Population Genetic Inference With MIGRATE

Population Genetic Inference

Members of the Department of Statistics will be teaching a graduate-level course of eight lectures introducing the theory and techniques of population genetic inference with particular emphasis on modern developments in the field of human genetics. Lectures of 90 minutes will be held at 2pm on Tuesdays in Lecture Theatre C in the Department of Zoology on South Parks Road. No registration is req...

Principles of population genetic inference

Population genetic inference from genomic sequence variation.

Population genetics has evolved from a theory-driven field with little empirical data into a data-driven discipline in which genome-scale data sets test the limits of available models and computational analysis methods. In humans and a few model organisms, analyses of whole-genome sequence polymorphism data are currently under way. And in light of the falling costs of next-generation sequencing...

Deep Learning for Population Genetic Inference

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the ...

Population genetic inference from resequencing data.

This article is concerned with statistical modeling of shotgun resequencing data and the use of such data for population genetic inference. We model data produced by sequencing-by-synthesis technologies such as the Solexa, 454, and polymerase colony (polony) systems, whose use is becoming increasingly widespread. We show how such data can be used to estimate evolutionary parameters (mutation an...