SMS scnews item created by Munir Hiabu at Wed 3 Jun 2020 1431
Type: Seminar
Distribution: World
Expiry: 5 Jun 2020
Calendar1: 5 Jun 2020 1400-1500
CalLoc1: https://au.bbcollab.com/collab/ui/session/guest/fcf219c74ac743e89565a9e6e8d349a9
CalTitle1: Inferring genetic linkage maps from high-throughput sequencing data
Auth: munir@119-18-2-42.771202.syd.nbn.aussiebb.net (mhia8050) in SMS-WASM

# Statistics Across Campuses : Matthew Schofield -- Inferring genetic linkage maps from high-throughput sequencing data

Title: Inferring genetic linkage maps from high-throughput sequencing data

Date: 05 June 2020, Friday

Time: 2pm

Speaker: Dr Matthew Schofield (University of Otago)

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.â€‹

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.