Identifying disease-causing haplotypes with hidden Markov models (Masters option available)

Identifying disease-causing haplotypes with hidden Markov models (Masters option available)

Project details

Many disease-causing mutations (DCM) have arisen in the past. Individuals who share the same ancestral DCM also share other markers around the DCM, i.e. a haplotype. These haplotypes can be modeled with a hidden Markov model (HMM). Several neurogenetic disorders have limited DCMs (Lomax, Brain 2013 136(4):1146 and Ishiura, Nat Genet 2018 50(4):581), which can be catalogued and searched for using any type of dense single nucleotide polymorphism (SNP) data.  

This project involves developing a database containing DCM haplotypes, developing methods to scan large datasets for such haplotypes, and analysing large neurogenetic datasets to identify individuals with DCMs. Students require strong quantitative and programming skills (R and C++), be comfortable with developing expertise in HMMs, and show an interest in genetics. 

About our research group

The Bahlo laboratory is a statistical genetics/bioinformatics laboratory working towards understanding the genetic causes of disease. The laboratory is highly collaborative, with six postdocs, one research assistant, two PhD students and two Masters students. It has a strong interest in statistical and population genomics and transcriptomic analysis. Statistical methods and accompanying software are developed as part of this work (see https://github.com/bahlolab and Freytag, Genom Med 2017 9(1):55 and Henden, PLOS Genet 2018 14(5):e1007279). The laboratory works closely with geneticists and clinicians and has well-established collaborations with neurologists, ophthalmologists and immunologists, which have led to the discovery of more than 10 novel epilepsy genes, as well as genes for MacTel, ataxias and other neurological disorders, and new insights into malaria drug resistance. 

Researchers:

Professor Melanie Bahlo

Melanie Bahlo
Professor
Melanie
Bahlo
Joint Division Head

Project Type: