Human genome research leaps ahead at SFU
A deeper understanding of how the human genome varies may help scientists and health care professionals predict the likelihood of conditions such as autism and mental retardation.
And a new algorithm from SFU's Lab for Computational Biology in the School of Computing Science is making international headlines among the genetics research community, including landing on the December 2011 cover of Genome Research. Working with worldwide researchers on the 1000 Genomes Project, the Lab for Computational Biology led by Professor Cenk Sahinalp combines discrete math, algorithms and molecular biology to predict genetic variation, especially large-scale variation, and to learn how these variations act as a precursor to developmental issues.
Sahinalp, together with doctorate students Fereydoun Hormozdiari and Iman Hajirasouliha, recently developed a streamlined method of comparing genomes without first comparing them to a reference genome to better predict genomic variations among closely related individuals, such as between a child and the parents.
Scientists conventionally look for genome variations in two steps. First, short pieces from each individual genome are compared with an assembled reference genome and its (structural) differences with respect to the reference are identified. The reference genome is a single genome sequenced through the well-known Human Genome Project, completed in the early 2000s. Next, the lists of structural variants in each genome are compared against each other.
In this study, SFU's Lab for Computational Biology moves away from the above two-step approach to one in which all genomes are compared with the reference genome simultaneously through a combinatorial optimization framework. This strategy yields a much higher accuracy in structural variation detection. As a result, the number of genetic variations between family members that were predicted at birth and that actually surfaced later in life were at least 20 times more accurate.
This implies much better accuracy in pinpointing the potential genomic causes of certain conditions, such as autism, in children who have healthy parents.