Evolutionary genomics can now be applied beyond ‘model’ organisms Technological advances brings genomics to the study of ecology & evolution But genomics has also made apparent the need for incorporating evolution into basic biological study Knowing how and why characters evolve (e.g. which residues of a protein are under constraint) informs on function (e.g. which residues are important) Brings genomics to study of ecology and evolution, when the organism is chosen NOT because it is easiest to work with (‘model’) but because it is the best organism to address a specific scientific question

Types of questions & comparisons in evolutionary genomics Given several whole-genome sequences, we can compare: * Genome size, organization (chromosomes/plasmids), structure * Gene/ncRNA content: number of genes, duplicates, size of gene families, etc * Sequence differences related to: gene evolution, regulatory evolution * RNA & protein abundance across species, for all RNAs/proteins Ultimately, many of us are interested in genomic features under selection: * Which genomic features are restricted from varying? Why? * Which genomic features are least restricted from varying? Why? * Which genomic features are/were involved in adaptation?

The How and the Why of Evolution We can observe what characters are different within and between species Using phylogeny, we can often reconstruct the common ancestral state Together these can inform on the history of changes The WHY: often much more challenging The goal is to understand the forces that drive changes or restrict change Often this means looking for known signatures of evolution (e.g. selection)

“Nothing in Biology Makes Sense Except in the Light of Evolution” T. Dobzhansky ‘Nothing in [comparative genomics] Makes Sense Except in the Light of the [phylogenetic tree]’ A. Gasch bastardization

Primer on Phylogeny Cladogram Phylogram a a b b c c d d e e Shows structure of the tree only Shows structure AND distance between nodes

4 incarnations of the SAME unrooted tree Primer on Phylogeny 4 incarnations of the SAME unrooted tree a a a b c d e b a b b c e c c d d d e e Unrooted means do not know history (i.e. where the common ancestor is)

Now distance also corresponds to ‘time’ (molecular clock theory) Primer on Phylogeny 3 incarnations of the SAME rooted tree a b c d e z a a b b c c * d d e * e z z known outgroup * Rooted means DO know history (i.e. where the common ancestor * is) Now distance also corresponds to ‘time’ (molecular clock theory) or order of events

Reconstructing the Ancestral State b c d * e z known outgroup

Reconstructing the Ancestral State Speciation event b c d * e z known outgroup In a species tree, each bifurcation represents a species split

Reconstructing the Ancestral State 4 b 3 c 2 d 5 1 * e z known outgroup Time/Distance Given a tree, we can reconstruct the ancestral state at each node. Usually work by Parsimony = smallest number of changes to explain the tree (i.e. the simplest explanation)

Reconstructing the Ancestral State Species can utilize: a Glucose, Galactose, Lactose 4 b 3 Glucose, Galactose, Lactose c 2 Glucose, Galactose, Lactose, Fructose d 5 Glucose, Galactose, Fructose 1 * e Glucose, Galactose, Fructose z Glucose, Lactose, Fructose Time/Distance Given a tree, we can reconstruct the ancestral state at each node. Usually work by Parsimony = smallest number of changes to explain the tree (i.e. the simplest explanation)

Reconstructing the Ancestral State Species can utilize: a Glucose, Galactose, Lactose GGL b GGLF Glucose, Galactose, Lactose c GGLF Glucose, Galactose, Lactose, Fructose d GGF Glucose, Galactose, Fructose GGLF or GLF * e Glucose, Galactose, Fructose z Glucose, Lactose, Fructose Time/Distance Events: Last common ancestor could have been either GGLF or GLF Galactose utilization might have been gained No change in states Fructose utilization was lost Lactose utilization was lost

several excellent courses on campus that cover this Making inferences based on phylogeny: Confidence in your inferences depends on how much you trust your tree Methods of phylogeny construction: * Neighbor joining: organize species based on similarity score - computationally the simplest but can be misleading especially if species are of variable evolutionary distances (“variable branch lengths”) * Parsimony: simplest tree to explain the observed data - simplest to model, can be computationally intensive without ‘heuristics’ * Maximum likelihood: requires specific models of evolution - computationally very intensive, need specific models * Baysian: - can be computationally intensive, need specific models ** Methods of phylogeny construction are beyond this course, but there are several excellent courses on campus that cover this

Making inferences based on phylogeny: Confidence in your inferences depends on how much you trust your tree Most methods have some way of assessing confidence at each node a Bootstrapping: Remake the tree 1,000 times using a subset of the data and see how many times you get the same node. High bootstrap value (>0.6) means in 60% of remade trees you observe that node. 1 b 0.6 c 0.4 d 1 1 * e z bootstrap values

Making inferences based on phylogeny: Confidence in your inferences depends on how much you trust your tree Most methods have some way of assessing confidence at each node Often represent the consensus tree a a 1 b 0.6 b c c 0.4 d d 1 1 * e e z z bootstrap values Collapse nodes without high confidence

Making inferences based on phylogeny: Confidence in your inferences depends on how much you trust your tree Most methods have some way of assessing confidence at each node a Baysian methods use a different approach Posterior Probability: Typically don’t trust nodes with <90% posterior probability. 1 b 0.6 c 0.4 d 1 1 * e z posterior probabilities

Making inferences based on phylogeny: Confidence in your inferences depends on how much you trust your tree Most methods have some way of assessing confidence at each node Consensus tree a a 1 b 0.6 b c c 0.4 d d 1 1 * e e z z posterior probabilities Collapse nodes with <0.9 posterior prob.

Species tree vs. gene/protein tree Trees can be very different, since genes can have their own histories Very important to know the difference between the trees! a. Gene tree is based a set of orthologous genes (i.e. related by a common ancestor) Often (but certainly not always) the gene tree is similar to the species tree b. Species tree is meant to represent the historical relationship between species. Want to build on characters that reflect time since divergence: In the genomic age, often use as many genes as possible (hundreds to thousands) to generate a species tree: Phylogenomics

Phylogenomics: Using Whole-genome information to reconstruct the Tree of Life Several approaches: 1. Concatonate many gene sequences and treat as one Use a ‘super matrix’ of variable sequence characters 2. Construct many separate trees, one for each gene, and then compare Often construct a ‘super tree’ that is built from all single trees 3. Incorporate non-sequence characters like synteny, intron structure, etc. The goal is to use many different # and types of characters to avoid being mislead about the relationship between species. Now recognized that different regions of the genome can have distinct histories.

A few other key basic concepts: Selection acts on phenotypes, based on their fitness cost/advantage, to affect the population frequencies of the underlying genotypes. In the case of DNA sequence: Neutral substitutions = no effect on fitness, no effect on selection Given a ~constant mutation rate, can convert the # of substitutions into time of divergence since speciation = molecular clock theory. Deleterious substitutions = fitness cost * These are removed by purifying (negative) selection Advantageous substitutions = fitness advantage * These alleles are enriched for through adaptive (positive) selection