Journal Article

Genotype–phenotype associations: substitution models to detect evolutionary associations between phenotypic variables and genotypic evolutionary rate

Timothy D. O'Connor and Nicholas I. Mundy

in Bioinformatics

Volume 25, issue 12, pages i94-i100
Published in print June 2009 | ISSN: 1367-4803
Published online May 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp231

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Motivation: Mapping between genotype and phenotype is one of the primary goals of evolutionary genetics but one that has received little attention at the interspecies level. Recent developments in phylogenetics and statistical modelling have typically been used to examine molecular and phenotypic evolution separately. We have used this background to develop phylogenetic substitution models to test for associations between evolutionary rate of genotype and phenotype. We do this by creating hybrid rate matrices between genotype and phenotype.

Results: Simulation results show our models to be accurate in detecting genotype–phenotype associations and robust for various factors that typically affect maximum likelihood methods, such as number of taxa, level of relevant signal, proportion of sites affected and length of evolutionary divergence. Further, simulations show that our method is robust to homogeneity assumptions. We apply the models to datasets of male reproductive system genes in relation to mating systems of primates. We show that evolution of semenogelin II is significantly associated with mating systems whereas two negative control genes (cytochrome b and peptidase inhibitor 3) show no significant association. This provides the first hybrid substitution model of which we are aware to directly test the association between genotype and phenotype using a phylogenetic framework.

Availability: Perl and HYPHY scripts are available upon request from the authors.

Contact: to252@cam.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6312 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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