Computational Methods for Detecting Large-Scale Chromosome Rearrangements in SNP Data

Jussi Kollin will defend his thesis 'Computational Methods for Detecting Large-Scale Chromosome Rearrangements in SNP Data' on Monday 25 October at 12 noon in the university's Main Building, auditorium XII. His custos is Professor Esko Ukkonen and opponent Docent Sampsa Hautaniemi.

Abstract:

Large-scale chromosome rearrangements such as copy number variants (CNVs)  and inversions encompass a considerable proportion of the genetic  variation between human individuals. In a number of cases, they have been  closely linked with various inheritable diseases. 

Single-nucleotide polymorphisms (SNPs) are another large part of the  genetic variance between individuals. They are also typically abundant and  their measuring is straightforward and cheap. 

This thesis presents computational means of using SNPs to detect the  presence of inversions and deletions, a particular variety of CNVs.  Technically, the inversion-detection algorithm detects the suppressed  recombination rate between inverted and non-inverted haplotype populations  whereas the deletion-detection algorithm uses the EM-algorithm to estimate  the haplotype frequencies of a window with and without a deletion  haplotype. As a contribution to population biology, a coalescent simulator  for simulating inversion polymorphisms has been developed. Coalescent  simulation is a backward-in-time method of modelling population ancestry.  Technically, the simulator also models multiple crossovers by using the  Counting model as the chiasma interference model. 

Finally, this thesis includes an experimental section. The aforementioned  methods were tested on synthetic data to evaluate their power and  specificity. They were also applied to the HapMap Phase II and Phase III  data sets, yielding a number of candidates for previously unknown  inversions, deletions and also correctly detecting known such  rearrangements.