We have picked two papers for the upcoming lab meeting next week. The first one is by Masafumi Nozawaa, Yoshiyuki Suzukia, and Masatoshi Nei and is entitled ‘Reliabilities of identifying positive selection by the branch-site and the site-prediction methods’
Abstract
Natural selection operating in protein-coding genes is often studied by examining the ratio (ω) of the rates of nonsynonymous to synonymous nucleotide substitution. The branch-site method (BSM) based on a likelihood ratio test is one of such tests to detect positive selection for a predetermined branch of a phylogenetic tree. However, because the number of nucleotide substitutions involved is often very small, we conducted a computer simulation to examine the reliability of BSM in comparison with the small-sample method (SSM) based on Fisher's exact test. The results indicate that BSM often generates false positives compared with SSM when the number of nucleotide substitutions is ≈80 or smaller. Because the ω value is also used for predicting positively selected sites, we examined the reliabilities of the site-prediction methods, using nucleotide sequence data for the dim-light and color vision genes in vertebrates. The results showed that the site-prediction methods have a low probability of identifying functional changes of amino acids experimentally determined and often falsely identify other sites where amino acid substitutions are unlikely to be important. This low rate of predictability occurs because most of the current statistical methods are designed to identify codon sites with high ω values, which may not have anything to do with functional changes. The codon sites showing functional changes generally do not show a high ω value. To understand adaptive evolution, some form of experimental confirmation is necessary.
Natural selection operating in protein-coding genes is often studied by examining the ratio (ω) of the rates of nonsynonymous to synonymous nucleotide substitution. The branch-site method (BSM) based on a likelihood ratio test is one of such tests to detect positive selection for a predetermined branch of a phylogenetic tree. However, because the number of nucleotide substitutions involved is often very small, we conducted a computer simulation to examine the reliability of BSM in comparison with the small-sample method (SSM) based on Fisher's exact test. The results indicate that BSM often generates false positives compared with SSM when the number of nucleotide substitutions is ≈80 or smaller. Because the ω value is also used for predicting positively selected sites, we examined the reliabilities of the site-prediction methods, using nucleotide sequence data for the dim-light and color vision genes in vertebrates. The results showed that the site-prediction methods have a low probability of identifying functional changes of amino acids experimentally determined and often falsely identify other sites where amino acid substitutions are unlikely to be important. This low rate of predictability occurs because most of the current statistical methods are designed to identify codon sites with high ω values, which may not have anything to do with functional changes. The codon sites showing functional changes generally do not show a high ω value. To understand adaptive evolution, some form of experimental confirmation is necessary.
And the second paper is by Thomas Lenormand, Denis Roze and François Rousset and is entitled ’Stochasticity in evolution’
Abstract
The debate over the role of stochasticity is central in evolutionary biology, often summarised by whether or not evolution is predictable or repeatable. Here we distinguish three types of stochasticity: stochasticity of mutation and variation, of individual life histories and of environmental change. We then explain when stochasticity matters in evolution, distinguishing four broad situations: stochasticity contributes to maladaptation or limits adaptation; it drives evolution on flat fitness landscapes (evolutionary freedom); it might promote jumps from one fitness peak to another (evolutionary revolutions); and it might shape the selection pressures themselves. We show that stochasticity, by directly steering evolution, has become an essential ingredient of evolutionary theory beyond the classical WrightFisher or neutralistselectionist debates.
You can contact me (Maren.wellenreuther@zooekol.lu.se) if you have problems retrieving the pdf files -and I will send them to you.
All the best and happy reading, Maren
The debate over the role of stochasticity is central in evolutionary biology, often summarised by whether or not evolution is predictable or repeatable. Here we distinguish three types of stochasticity: stochasticity of mutation and variation, of individual life histories and of environmental change. We then explain when stochasticity matters in evolution, distinguishing four broad situations: stochasticity contributes to maladaptation or limits adaptation; it drives evolution on flat fitness landscapes (evolutionary freedom); it might promote jumps from one fitness peak to another (evolutionary revolutions); and it might shape the selection pressures themselves. We show that stochasticity, by directly steering evolution, has become an essential ingredient of evolutionary theory beyond the classical WrightFisher or neutralistselectionist debates.
You can contact me (Maren.wellenreuther@zooekol.lu.se) if you have problems retrieving the pdf files -and I will send them to you.
All the best and happy reading, Maren
PS: Any Fika volunteers? Please send me an email if you are bringing something to the meeting.
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