Current computational methods for prioritizing candidate regulatory polymorphisms

Detection and impact of rare regulatory variants in human disease

Advances in genome sequencing are providing unprecedented resolution of rare and private variants. However, methods which assess the effect of these variants have relied predominantly on information within coding sequences. Assessing their impact in non-coding sequences remains a significant contemporary challenge. In this review, we highlight the role of regulatory variation as causative agents and modifiers of monogenic disorders. We further discuss how advances in functional genomics are now providing new opportunity to assess the impact of rare non-coding variants and their role in disease.

Detecting gene duplications in the human lineage

Gene duplications represent an important class of evolutionary events that is likely to have contributed to the unique human phenotype in the short evolutionary time since the human-chimpanzee divergence. With the availability of both human and chimpanzee genome drafts in high coverage re-sequencing assemblies and the high annotation quality of most human genes, it should now be possible to identify all human lineage-specific gene duplication events (human inparalogues) and a few pioneering studies have attempted to do that. However, the different levels of coverage in the human and chimpanzee's genomes assemblies, and the differing levels of gene annotation, have led to problematic assumptions and oversimplifications in the algorithms and the datasets used to detect human lineage-specific gene duplications. In this study, we have developed a set of bioinformatic tools to overcome a number of the conceptual problems that are prevalent in previous studies and have collected a reliable and representative set of human inparalogues.