New method to identify genes affecting health
A new tool which makes it possible to extract information about an individual's health from genotypes in a fraction of a second, has been developed by an ECS academic.
In a paper entitled Boosting Haplotype Inference with Local Search, just published in Constraints: An International Journal, Professor Joao Marques-Silva, of the University's School of Electronics and Computer Science, describes with collaborators a new approach to the process of inferring haplotype information from genotype data.
A haplotype can be defined as a group of alleles of one or more genes on a single chromosome that are closely enough linked to be inherited usually as a unit and a genotype refers to the combination of alleles inherited from both parents.
According to Professor Marques-Silva, the current method of extracting haplotypes from genotype data is based on statistical approaches, which can take a long time to compute.
Professor Marques-Silva and collaborators approached this scenario by taking the Haplotype Inference by Pure Parsimony (HIPP), a solution that minimises the total number of distinct haplotypes used, and developed new algorithms which they applied to achieve faster results.
'Biologists have been using these statistical approaches for a long time and may not be open to change,' he said. 'However, these methods can take days, even months to terminate, whereas our approach produces an almost instant result.'
Further research is being carried out currently by Professor Marques-Silva and collaborators to validate this new method and to prove that it could replace statistical methods in a number of settings.
'This is the biggest development that we have made in this field so far,' said Professor Marques-Silva. 'It remains to be seen whether biologists will use this instead of existing techniques.'