A phylogenetic method linking nucleotide substitution rates to rates of continuous trait evolution
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Abstract
Genomes contain conserved non-coding sequences that perform important biological functions, such as gene regulation. We present a phylogenetic method, PhyloAcc-C, that associates nucleotide substitution rates with changes in a continuous trait of interest. The method takes as input a multiple sequence alignment of conserved elements, continuous trait data observed in extant species, and a background phylogeny and substitution process. Gibbs sampling is used to assign rate categories (background, conserved, accelerated) to lineages and explore whether the assigned rate categories are associated with increases or decreases in the rate of trait evolution. We test our method using simulations and then illustrate its application using mammalian body size and lifespan data previously analyzed with respect to protein coding genes. Like other studies, we find processes such as tumor suppression, telomere maintenance, and p53 regulation to be related to changes in longevity and body size. In addition, we also find that skeletal genes, and developmental processes, such as sprouting angiogenesis, are relevant. The R/C++ software package implementing our method is available under an open source license from https://github.com/phyloacc/PhyloAcc-C .
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e also found associations top53, telomere maintenance, and cell fate within 1 Mbp of our top 25 loci of interest. Ourtop 25 loci also have links to cancer and height or body size, though these prevalent diseasesand biomarkers are of course heavily studied and consequently commonly annotated, and sowe cannot know whether their appearance is simply due to their frequency
Is this 1Mbp in either direction of a loci of interest? Just binning the human genome by 25 points gives about 1.7% of the genome within 1Mbp of these uniform bins. Depending on what percent of genes are associated with the traits of interest that could be very rare, or fairly common. Is there a way of viewing how impactful this result is in comparison to the size of the genome annotated as relevant to these traits?
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