Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Jan 1;28(1):48-55.
doi: 10.1093/bioinformatics/btr592. Epub 2011 Oct 28.

Determining the Evolutionary History of Gene Families

Affiliations

Determining the Evolutionary History of Gene Families

Ryan M Ames et al. Bioinformatics. .

Abstract

Motivation: Recent large-scale studies of individuals within a population have demonstrated that there is widespread variation in copy number in many gene families. In addition, there is increasing evidence that the variation in gene copy number can give rise to substantial phenotypic effects. In some cases, these variations have been shown to be adaptive. These observations show that a full understanding of the evolution of biological function requires an understanding of gene gain and gene loss. Accurate, robust evolutionary models of gain and loss events are, therefore, required.

Results: We have developed weighted parsimony and maximum likelihood methods for inferring gain and loss events. To test these methods, we have used Markov models of gain and loss to simulate data with known properties. We examine three models: a simple birth-death model, a single rate model and a birth-death innovation model with parameters estimated from Drosophila genome data. We find that for all simulations maximum likelihood-based methods are very accurate for reconstructing the number of duplication events on the phylogenetic tree, and that maximum likelihood and weighted parsimony have similar accuracy for reconstructing the ancestral state. Our implementations are robust to different model parameters and provide accurate inferences of ancestral states and the number of gain and loss events. For ancestral reconstruction, we recommend weighted parsimony because it has similar accuracy to maximum likelihood, but is much faster. For inferring the number of individual gene loss or gain events, maximum likelihood is noticeably more accurate, albeit at greater computational cost.

Availability: www.bioinf.manchester.ac.uk/dupliphy

Contact: simon.lovell@manchester.ac.uk; simon.whelan@manchester.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Similar articles

See all similar articles

Cited by 13 articles

See all "Cited by" articles

Publication types

LinkOut - more resources

Feedback