Estimating the tempo and mode of gene family evolution from comparative genomic data.

By Matthew W. Hahn, Tijl De Bie, Jason E. Stajich, Chi-Thoi Nguyen, Nello Cristianini. Genome Research, 2005

310 citations recorded by the source corpus.

Comparison of whole genomes has revealed that changes in the size of gene families among organisms is quite common. However, there are as yet no models of gene family evolution that make it possible to estimate ancestral states or to infer upon which lineages gene families have contracted or expanded. In addition, large differences in family size have generally been attributed to the effects of natural selection, without a strong statistical basis for these conclusions. Here we use a model of stochastic birth and death for gene family evolution and show that it can be efficiently applied to multispecies genome comparisons. This model takes into account the lengths of branches on phylogenetic trees, as well as duplication and deletion rates, and hence provides expectations for divergence in gene family size among lineages. The model offers both the opportunity to identify large-scale patterns in genome evolution and the ability to make stronger inferences regarding the role of natural selection in gene family expansion or contraction. We apply our method to data from the genomes of five yeast species to show its applicability.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/af3106d5f9488df7a0f465eb584f00ff7d0b8f13

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

In Plant Science

Citations in the corpus

Ancient Protostome Origin of Chemosensory Ionotropic Glutamate Receptors and the Evolution of Insect Taste and Olfaction (2010, methodology): “To estimate the number of gene gain and loss events for each orthologous group we estimated gene numbers on internal branches using a maximum likelihood method [82] implemented in the software CAFÉ [83].”
Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3. (2013, methodology): “Our previous approach to estimating this rate modeled the gain and loss of genes within a gene family using a birth-anddeath stochastic process (Hahn et al. 2005).”
Mutations in smooth muscle α-actin (ACTA2) lead to thoracic aortic aneurysms and dissections (2007, methodology): “We used a recently developed maximum-likelihood model of birth-death evolution in gene familie”
Comparative genomics reveals insights into avian genome evolution and adaptation (2014, methodology): “For purposes of comparison, we used a stochastic birth-death model of gene family evolution to estimate rates of gene turnover in the globin gene families of birds and mammals (133).”