Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Research article

Differences in the evolutionary history of disease genes affected by dominant or recessive mutations

Simon J Furney13, M Mar Albà2 and Núria López-Bigas1*

Author Affiliations

1 Genome Bioinformatics Laboratory. Centre for Genomic Regulation, Universitat Pompeu Fabra, Pg. Maritim de la Barceloneta 37-49, E-08003, Barcelona, Spain

2 ICREA – Institut Municipal d'Investigació Mèdica. Universitat Pompeu Fabra, Dr. Aiguader 80, 08003, Barcelona, Spain

3 Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland

For all author emails, please log on.

BMC Genomics 2006, 7:165  doi:10.1186/1471-2164-7-165

Published: 3 July 2006



Global analyses of human disease genes by computational methods have yielded important advances in the understanding of human diseases. Generally these studies have treated the group of disease genes uniformly, thus ignoring the type of disease-causing mutations (dominant or recessive). In this report we present a comprehensive study of the evolutionary history of autosomal disease genes separated by mode of inheritance.


We examine differences in protein and coding sequence conservation between dominant and recessive human disease genes. Our analysis shows that disease genes affected by dominant mutations are more conserved than those affected by recessive mutations. This could be a consequence of the fact that recessive mutations remain hidden from selection while heterozygous. Furthermore, we employ functional annotation analysis and investigations into disease severity to support this hypothesis.


This study elucidates important differences between dominantly- and recessively-acting disease genes in terms of protein and DNA sequence conservation, paralogy and essentiality. We propose that the division of disease genes by mode of inheritance will enhance both understanding of the disease process and prediction of candidate disease genes in the future.