Inferring the nature of evolutionary relationship
   A complete evolutionary "tree" describes position of any 'twig', with respect to all others
       Optimization criterion: How to choose 'correct ' solution

       Distance: amount of evolutionary change between twigs
          Or: How similar (alike) are they?
              phenetic: distance measured between tips
                           "As the crow flies" from one twig to another
              patristic: distance measured along connecting branches
                          "As the ant runs" from one twig to another
      Relationship: pattern of connection between twigs
           How closely related are species?
               cladistic relationship: "As the branches join" back to
                        Most Recent Common Ancestor (MRCA)
                        How do twigs join lower 'stems', 'branches', 'limbs', etc. in tree?

   Phenetic & Cladistic criteria agree, iff rates of evolution are constant
      If evolutionary rates differ, closely related organisms appear dissimilar
       Ex.: Crocodilia more similar to Squamata (lizards & snakes) BUT more closely related to birds
                 Historically: Reptilia include scaly, four-legged crocs, lizards & snakes, turtles & tortoises
                                    Aves include feathery, two-legged, two-winged creatures with evolved adaptation for flight

   Likelihood: Given a model of molecular evolutionary change, which tree is least unlikely (maximally likely)?
                       Math known, computationally unfeasible until recently.

    Theoretical & technical breakthroughs late 1960s ~ 1990s ~ 21st cent.:
        Theory of Phylogenetic Systematics formalized
        Molecular data (allozymes & DNA) replace morphology as primary data for phylogenetic inference
                Computational power increases
                DNA sequencing capacity increases

      ***Patterns of evolutionary relationship to be understood from molecular data;
                then, Patterns of organismal evolution to be analyzed based on relationships ***

      Patterns of similarity inferred from UPGMA cluster analysis
         [Unweighted Pair Group Method, Arithmetic averaging],
            Sequential Agglomerative Hierarchical Nesting (SAHN) algorithm
           algorithm: set of instructions for repetitive task
                 In (n) x (n) matrix, join most similar pair:
                  re-calculate (n-1) x (n-1) matrix, re-join,
                     & so on, until last pair joined
      Clustering results shown as phenogram:
                   diagram of phenetic similarity
                  Similarity estimates relationships under certain assumptions

       UPGMA method assumes rates of evolution equal
            so branch tips "come out even" (contemporaneous)
            Rate differences lead to incorrect trees

       HOMEWORK: Practice problems for UPGMA phenogram calculations

Alternative phenetic methods
       Neighbor-Joining (NJ) analysis does not assume rate equality
             NJ allows branch lengths proportional to change: tips come out uneven
                 algorithm joins nodes, rather than tips
            More realistic, recognizes stochastic "Molecular Clock"                  

       Differential weighting of nucleotide substitutions
            accord greater 'significance' to certain classes of change
         Ex.: Kimura 2-parameter (K2P) model treats Transitions (Ts) & Transversions (Tv) differently
                K  Transition Bias  = [Ts] / [Tv]
                       Twice as many kinds of Tv as Ts: expect K = 0.5                            
                But: Tv rare for close comparisons,
                            more common for distant relationships
                Set K according to nature of evolutionary problem under consideration:
                     K = 1 for close comparisons, K = 3 for moderate comparison
                    K = 10 or Tv-only for distant comparisons

     Choice of preferred hypothesis made on Principle of Maximum Parsimony
            Parsimony: simpler hypothesis preferred
            Ex.: If complex trait occurs in multiple species,
                         more parsimonious to hypothesize it evolved only once
                    => Trait evolved in single common ancestor

             Ex.: Evolution of ice-breeding in Phocidae ("True" seals),
                        from ecological & molecular parsimony perspectives

           Evolutionary parsimony:
                Hypothesis that requires fewest character changes preferred
                In molecular systematics, which requires fewest SNP changes  

      "Four-Taxon Problem" & "Three-Taxon Statement":
         Four taxa A, B, C, & D have three hypotheses of relationship:
            A most closely related to B, or C, or D
 
         Evaluate alternative hypotheses as:
       "X and Y are more closely related to each other than either is to Z"
         Alternative hypotheses shown as networks with branches & internode

Count changes at  informative SNPs that favor each hypothesis
      Hypothesis that requires fewest changes is Maximum Parsimony explanation:
         AKA 'Minimum Length' or 'Minimum Spanning' solution

      Modifications: use K2P criteria, weight # changes by K = [Tv] / [Ts]
                             Protein Parsimony: count amino acid substitutions
                                                               Count 1st & 2nd position SNPs only
                    HOMEWORK: What triplets are exceptions & why?

  Evolutionary trees are networks with roots
      With four taxa, network has four branches & one internode
      Root indicates relationship with common ancestor
          'root' can be placed on any branch or internode
          Thus five possible rooted trees (cladogram) for four-taxon network
              All equally parsimonious:
                not all place A & B as each others closest relatives
                Some make shared SNPs symplesiomorphic

      Outgroup rooting
         Include taxon known to be less closely related
            to any ingroup taxon than they are to each other
            Call this an outgroup
                 Ex.: Use feliform as outgroup to caniform problem
                        Note cladistic tree has same topology as NJ phenogram
                 Ex. Wolffish (Anarhichas): Johnstone et al. (2007)

HOMEWORK: Practice four-taxon cladistic problems

Maximum Likelihood analysis

    Different approach to evolutionary trees based on Bayes Theorem

        Given estimates of all possible SNP rates among A, C, G, & T (n = 12)
             Calculate probability of simultaneous occurrence
                    of all events necessary to produce any particular tree
             Any particular tree is (extremely) unlikely,
                    but one tree is least unlikely ( = maximally likely)
                    Ratio of likelihoods expresses how much better wrt any other

        Heuristic example: five-card stud poker with standard 52-card deck

Statistical tests determine confidence in branching order
          Bootstrap Analysis: a re-sampling technique
                statistical tests usually involve replication / repetition of experiment:
                this is (?) inconvenient with DNA data
            Suppose sample data set of n bases accurately estimates parametric data (complete genome)
                  re-sample n sites (with replacement) ~3,000 times
                        repeat phylogenetic analysis on each 'new' set:
                        among all of these sets,
                              how often do same clades / clusters appear?
                    "50% bootstrap support" identifies groups that occur more frequently than all others combined

Download & install MEGA [Molecular Evolutionary Genetic Analysis] software [Version 11 as of November 2024]

GenBank links to Carnivora / Primata

Lab Exercise: Are Giant Pandas (Ailuropoda) and Red (Lesser) Pandas (Ailurus) each others closest relatives?
        1,140 bp Cytochrome b data set (.meg format)
      15,582 bp mtDNA Coding Region data set (.meg format)  (ZIP file)
     15,600 bp mtDNA Coding Region, 12 taxonomic families (.meg format) (annotation)

HOMEWORK: Results for the Panda Problem

                from UPGMA, Neighbor Joining, Maximum Parsimony, & Maximum Likelihood methods