Genetic mapping, haplotyping and relationship estimation

Genetic mapping, haplotyping and relationship estimation

This theme includes the development of novel statistical methods for genetic mapping of complex traits, and applications to real data. The work started from mapping quantitative trait loci (QTL), designing methods for the analysis of inbred and outbred plant and animal populations, but the current focus is now mainly on analysing genetic multifactorial disease traits in humans, using family data. The statistical approach is based on hierarchial Bayesian modelling, and on algorithmic Markov chain Monte Carlo sampling in the numerical work.A general methodology for haplotyping and relationship estimation is targeted at applications in the genetic mapping of multifactorial traits. The approach uses simulated reconstructions of ancestral pedigrees and corresponding gene flows, conditioned on data arising from a study sample of present day genotyped individuals.In another project, Bayesian association-based fine mapping methods for quantitative and binary traits has been developed. This project aims to select trait-associated subset of markers among large number of candidates either in wide chromosomal segments or in small candidate regions.

Contact: Elja Arjas, Dario Gasbarra, Mikko Sillanpää, Matti Pirinen