Inferring genetic linkage maps from high-throughput sequencing data

Abstract: 

Genetic maps are usually the starting point for many types of genetic analysis. They are one-dimensional representations of genetic inheritance across a chromosome. Genetic maps frequency are commonly inferred from estimates of a hidden Markov model (HMM) since only the expression and not the transmission of genetic information is observed. No general approaches exist for assessing the uncertainty of the map. 

In this talk, we will obtain genetic maps and associated uncertainty for data arising from high-throughput sequencing (HTS). HTS technology provides high density data from a large numbers of individuals in a cost- and time-efficient manner. However, the observed data from HTS are more error prone than previous technologies. We first extend the HMM to account for error introduced by HTS. We then use a Bayesian approach to obtain reliable measures of uncertainty for many features of the resulting map.​

Bio: Matt Schofield is a Senior Lecturer at the University of Otago, via a postdoc with Andy Gelman at Columbia, whose primary research interests involve applying Bayesian hierarchical models to challenging programs in ecology, starting with capture-recapture modelling.  He publishes across top journals in statistics and ecology, proposing ways forward when analysing new data types, and providing new perspectives on older problems.