“Nothing in biology makes sense except in the light of evolution.”

1. “Nothing in biology makes sense except in the light of evolution.” “Scientists often have a naive faith that if only they could discover enough facts about a problem, these facts would somehow arrange themselves in a compelling and true solution.” Theodosius Dobzhansky 1900-1975

2. Good sources of information on molecular phylogenetics and tree reconstruction Freeman and Herron, 4th ed Hillis, Moritz, Mable, 2nd ed Hartl and Clark, 4th ed

3. Phylogenetic Estimation • Deriving hypotheses about the of evolutionary history of lineages based on molecular data • Species delineation • Phylogeography • Character evolution • Lots more

4. Pedagogical Considerations • Phylogenetics is an interdisciplinary science • Genetics • Evolutionary processes (multiple levels) • Life history and natural history of organisms • Geological history • Statistics and mathematical algorithms

5. Intro to Phylogenetics Types of characters: nuclear DNA mt DNA restriction fragment data (RFLPs) DNA fingerprinting (microsatellites) proteins (allozymes, aa sequence)

6. Intro to Phylogenetics Samples or representations of genetic material to capture “phylogenetic signal” Types of characters: nuclear DNA Mt/chlor DNA restriction fragment data (RFLPs) DNA fingerprinting (microsatellites) proteins (allozymes, aa sequence)

7. Phylogenetics in the genomics age… • Extensions to genomic-level analyses and questions: • Genome-wide sequence divergence and phylogenetic analysis--whole mtDNA genome vs. parts • How many bp resolves best phylogenetic hypotheses? • How are mtDNA and nuclear DNA variation related? • Does mtDNA sequence diversity within lineages correlate with genome size variation? • Can you use functional/structural protein sequences for phylogenetic analyses if you survey enough of them?

8. Genetic diversity/no morphological diversity Plethodontid salamanders P. hubrichti RM 1 RM 2 N1 BM 1 BM 2 DG VA 1 GF 1 GF 2 WT VA1 DG VA 2 WT VA 2 N2 Desmognathus wrighti (pygmy salamander) N1 SI 1 SI 2 CW 1 CW 2 S1 N2 RBB 1 RBB 2 S3 BR 1 CM 1 CM 2 BR 2 PG 1 SM. 1 SM. 2 S2 S2 S1 PG 2 ML 1 ML 2 CD 1 CD 2 S3 mt DNA sequence Combination mtDNA and allozyme

9. Morphological diversity/no genetic diversity Finches and widowbirds Sexual dimorphism Resource partitioning Nutritional effects Recent isolation Founder effect

10. Genomic/mt DNA extraction PCR target gene sequence DNA sequence Alignment Phylogenetic analysis

11. Assumptions of phylogenetic analyses • Common descent • Characters must reflect genetic inheritance • Characters evolve independently • No homoplasy • i.e., event-by- event recounting of fixed mutations in a lineage over time • No polarity in character states unless an outgroup is specified (based on other types of data) • Intertaxon variation > intrataxon variation

12. Forefathers of Phylogenetics Charles Darwin (1809-1882) Sewell Wright (1889-1988) Motoo Kimura (1924-1994)

13. Neutral Theory Paradigm • The majority of base substitutions that become fixed in populations are neutral with respect to fitness • Regions of genome that are under selection are not appropriate for detection of phylogenetic signal • Genetic mutation is the source of genetic variation • Genetic drift dominates evolution at the level of DNA sequence

14. Mutation • Heritable change in genetic code • Point mutation • Insertions/deletions (recombination) • Transposable elements • Mutation rates are not equal throughout the genome

15. Variation in mutation rates among genomic regions • Coding sequences (exons, code for proteins) • Non-coding sequences (introns) • Regulatory regions (5’UTR, 3’UTR, promoters) • Pseudogenes (non-functional gene relicts) • Wobble position nucleotides • Synonymous (silent) vs. non-synonymous (replacement) • Variation due to function of protein product Hartl & Clark, Principles of Population Genetics

16. Kinds of mutations occur at different rates • Genes/regions that best detect phylogenetic signal conform to neutral theory predictions • Models of evolution are used to incorporate variation in mutation rates within the data (based on molecular genetic processes) for more realistic estimations of evolutionary history Hartl & Clark, Principles of Population Genetics

17. Molecular clocks • Implicitly used when choosing a region to assay for variation given the expected evolutionary distance of interest • Explicitly used when attempting to date divergence times • Need to calibrate divergence times estimated with DNA variation with historical geological dates/events • Lots of debate and criticism about the use of molecular clocks

18. Molecular clocks Hartl & Clark, Principles of Population Genetics

19. Molecular clocksWhen is a molecule not appropriate? Saturation (homoplasy)

20. Molecular clocksWhen is a molecule not appropriate? Questions to ask yourself Do molecular clocks tick evenly through time? Is there a geological date/event for calibration? Are geological calibrations useful? Molecules can evolve at different rates than organisms (or other molecules)!

21. 28S rRNA partial sequence Forward primer • Alignment rules of thumb: • Assumption that similarity in sequence reflects homology • Best to use the same number of characters across operational taxonomic units (OTUs) • Gaps are problematic for algorithms even though they may be evolutionarily important - minimize gaps-check for reliability of sequence, etc. - can be considered a 5th character state or included in some way in analysis in some programs. Reverse primer

22. Masatoshi Nei (father of our favorite phylogenetic statistics) Forefathers of Phylogenetic Analyses Willi Hennig (father of cladistics) Joseph Felsenstein (phylogenetic algorithms)

23. Basic steps of phylogenetic estimation… Define specific sequence of steps (algorithm) for constructing the best tree from a set of possible phylogenies Define criteria for comparing alternate phylogenies to determine which is best (optimality criteria statistic)

24. Types of tree construction methods… • Maximum Parsimony • based on only characters that vary among sequences • calculates the most efficient tree length (tree value is the least number of changes to create phylogeny) Distance Methods (minimum evolution) based on calculated pairwise distance statistics the smallest value of the sum of all branches as an estimate of the correct tree (additive tree) computation intensity • Maximum Likelihood** • Bayesian Analyses** ** beyond scope of MEGA, most undergraduates

25. Distance matrix OTU1 OTU2 .256 OTU3 .056 .139 OTU4 .176 .222 .312 Distance methods Kinds: UPGMA, Neighbor-Joining, Wagner, etc. additive (e.g., neighbor joining) or ultrameric (UPGMA) • Pros: • uses similarity and differences in measure • simple to calculate and faster to compute • statistical methods to evaluate trees • can estimate genetic distances from branch lengths • Cons: • doesn’t take into consideration models of evolution • reduced phylogenetic information

26. Pros: • Follows philosophy of evolutionary theory--intuitive • Multiple data sets (genes) can be combined in one analyses • statistical methods to evaluate trees • can estimate genetic distances from branch lengths • Cons: • doesn’t take into consideration sophisticated models of evolution as Max. Likelihood • Only uses parsimony informative characters (differences) Maximum Parsimony Moderate computing intensity  Exhaustive searches most intense (all trees are found and evaluated)  Heuristic searches (not all trees are found and evaluated independently) - branch and bound, closest neighbor swapping, min-mini algorithm

27. 1. Bootstrapping • Reordering data with replacement • Repeating 500-1000 times • Statistical probability of node formation • Strong phylogenetic signals should form nodes despite this rearrangement • Parsimony, neighbor joining, minimum evolution • 2, Compare total branch lengths among trees • neighbor joining and minimum evolution algorithms 3. Interior Branch Length Test • Are interior branch lengths significantly different than 0 using standard errors (maybe a node should be trifurcating)? • Neighbor joining and minimun evolution algorithms Statistical tests for reliability of tree Are nodes found repeatedly and not due to chance arrangements?

28. Consensus and collapsed trees Collapse uncertain nodes

29. Consensus and collapsed trees Collapse uncertain nodes

30. Consensus vs. Combination

31. New topologies and gene trees Gene tree of CRF family peptides in vertebrates Boorse and Denver, 2005

32. Phylogenetics software 383 phylogeny packages and 52 free servers PAUP PHYLIP MacClade Mesquite MrBayes MEGA http://evolution.genetics.washington.edu/phylip/software.html

33. MEGA tutorial Importing sequences Alignment Sequence statistics Phylogenetic estimation Visualization of trees