Lecture 4. Models of DNA and protein change. Likelihood methods

Lecture 4. Models of DNA and protein change. Likelihood methods Joe Felsenstein Department of Genome Sciences and Department of Biology Lecture 4. Models of DNA and protein change. Likelihood methods p.1/36

The Jukes-Cantor model (1969) A u/3 u/3 G u/3 u/3 u/3 C u/3 T the simplest symmetrical model of DNA evolution Lecture 4. Models of DNA and protein change. Likelihood methods p.2/36

Transition probabilities under the Jukes-Cantor model All sites change independently All sites have the same stochastic process working at them Make up a fictional kind of event, such that when it happens the site changes to one of the 4 bases chosen at random (equiprobably) Assertion: Having these events occur at rate 4 3u is the same as having the Jukes-Cantor model events occur at rate u The probability of none of these fictional events happens in time t is exp( 4 3 ut) No matter how many of these fictional events occur, provided it is not zero, the chance of ending up at a particular base is 1 4. Lecture 4. Models of DNA and protein change. Likelihood methods p.3/36

Jukes-Cantor transition probabilities, cont d Putting all this together, the probability of changing to C, given the site is currently at A, in time t is Prob (C A, t) = 1 4 (1 e 4 3 ut) while Prob (A A, t) = e 4 3 t + 1 4 (1 e 4 3 ut) or Prob (A A, t) = 1 4 (1 + 3e 4 3 ut) so that the total probability of change is (1 e 4 3 ut ) Prob (change t) = 3 4 Lecture 4. Models of DNA and protein change. Likelihood methods p.4/36

Fraction of sites different, Jukes-Cantor 1 Differences per site 0.75 0.49 0 0 0.7945 Branch length after branches of different length, under the Jukes-Cantor model Lecture 4. Models of DNA and protein change. Likelihood methods p.5/36

Kimura s (1980) K2P model of DNA change, A a G b b b b C a T which allows for different rates of transitions and transversions, Lecture 4. Models of DNA and protein change. Likelihood methods p.6/36

Motoo Kimura Motoo Kimura, with family in Mishima, Japan in the 1960 s Lecture 4. Models of DNA and protein change. Likelihood methods p.7/36

Transition probabilities for the K2P model with two kinds of events: I. At rate α, if the site has a purine (A or G), choose one of the two purines at random and change to it. If the site has a pyrimidine (C or T), choose one of the pyrimidines at random and change to it. II. At rate β, choose one of the 4 bases at random and change to it. By proper choice of α and β one can achieve the overall rate of change and T s /T n ratio R you want. For rate of change 1, the transition probabilities (warning: terminological tangle). ) Prob (transition t) = 1 4 1 2 ( exp 2R+1 R+1 t Prob (transversion t) = 1 2 1 2 exp ( 2 R+1 t ). + 1 4 exp ( 2 R+1 t ) (the transversion probability is the sum of the probabilities of both kinds of transversions). Lecture 4. Models of DNA and protein change. Likelihood methods p.8/36

Transitions, transversions expected Differences 0.60 0.50 0.40 0.30 Total differences Transitions 0.20 Transversions 0.10 0.00 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Time (branch length) R = 10 in different amounts of branch length under the K2P model, for T s /T n = 10 Lecture 4. Models of DNA and protein change. Likelihood methods p.9/36

Transitions, transversions expected 0.70 0.60 Total differences Differences 0.50 0.40 0.30 0.20 Transversions Transitions 0.10 0.00 R = 2 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Time (branch length) in different amounts of branch length under the K2P model, for T s /T n = 2 Lecture 4. Models of DNA and protein change. Likelihood methods p.10/36

Other commonly used models include: Two models that specify the equilibrium base frequencies (you provide the frequencies π A, π C, π G, π T and they are set up to have an equilibrium which achieves them), and also let you control the transition/transversion ratio: The Hasegawa-Kishino-Yano (1985) model: to : A G C T from : A απ G + βπ G απ C απ T G απ A + βπ A απ C απ T C απ A απ G απ T + βπ T T απ A απ G απ C + βπ C Lecture 4. Models of DNA and protein change. Likelihood methods p.11/36

My F84 model to : A G C T from : A απ G + β π G πr απ C απ T G απ A + β π A πr απ C απ T C απ A απ G απ T + βπ T π Y T απ A απ G απ C + β π C πy where π R = π A + π G and π Y = π C + π T (The equilibrium frequencies of purines and pyrimidines) Both of these models have formulas for the transition probabilities, and both are subcases of a slightly more general class of models, the Tamura-Nei model (1993). Lecture 4. Models of DNA and protein change. Likelihood methods p.12/36

Reversibility P ij Pji π j π i Lecture 4. Models of DNA and protein change. Likelihood methods p.13/36

The General Time-Reversible model (GTR) It maintains detailed balance" so that the probability of starting at (say) A and ending at (say) T in evolution is the same as the probability of starting at T and ending at A: to : A G C T from : A απ G βπ C γπ T G απ A δπ C ɛπ T C βπ A δπ G υπ T T γπ A ɛπ G υπ C And there is of course the general 12-parameter model which has arbitrary rates for each of the 12 possible changes (from each of the 4 nucleotides to each of the 3 others). (Neither of these has formulas for the transition probabilities, but those can be done numerically.) Lecture 4. Models of DNA and protein change. Likelihood methods p.14/36

Relation between models There are many other models, but these are the most widely-used ones. Here is a general scheme of which models are subcases of which other ones: General 12 parameter model (12) General time reversible model (9) Tamura Nei (6) HKY (5) F84 (5) Kimura K2P (2) Jukes Cantor (1) Lecture 4. Models of DNA and protein change. Likelihood methods p.15/36

Rate variation among sites In reality, rates of evolution are not constant among sites. Fortunately, in the transition probability formulas, rates come in as simple multiples of times Thus if we know the rates at two sites, we can compute the probabilities of change by simply, for each site, multiplying all branch lengths by the appropriate rate If we don t know the rates, we can imagine averaging them over a distribution of rates. Usually the Gamma distribution is used In practice a discrete histogram of rates approximates the integration (For the Gamma it seems best to use Generalized Laguerre Quadrature to pick the rates and frequencies in the histogram). Also, there are actually autocorrelations with neighboring sites having similar rates of change. This can be handled by Hidden Markov Models, which we cover later. Lecture 4. Models of DNA and protein change. Likelihood methods p.16/36

A pioneer of protein evolution Margaret Dayhoff, about 1966 Lecture 4. Models of DNA and protein change. Likelihood methods p.17/36

Models of amino acid change in proteins There are a variety of models put forward since the mid-1960 s: 1. Amino acid transition matrices Dayhoff (1968) model. Tabulation of empirical changes in closely related pairs of proteins, normalized. The PAM100 matrix, for example, is the expected transition matrix given 1 substitution per position. Jones, Taylor and Thornton (1992) recalculated PAM matrices (the JTT matrix) from a much larger set of data. Jones, Taylor, and Thurnton (1994a, 1994b) have tabulated a separate mutation data matrix for transmembrane proteins. Koshi and Goldstein (1995) have described the tabulation of further context-dependent mutation data matrices. Henikoff and Henikoff (1992) have tabulated the BLOSUM matrix for conserved motifs in gene families. 2. Goldman and Yang (1994) pioneered codon-based models (see next screen). Lecture 4. Models of DNA and protein change. Likelihood methods p.18/36

Approaches to protein sequence models Making a model for protein sequence evolution (a not very practical approach) 1. Use a good model of DNA evolution. 2. Use the appropriate genetic code 3. When an amino acid changes, accept it with a probability that declines as the amino acids become more different 4. Fit this to empirical information on protein evolution 5. Take into account variation of rate from site to site 6. Take into account correlation of rate variation in adjacent sites 7. How about protein structure? Secondary structure? 3 D struncture? Lecture 4. Models of DNA and protein change. Likelihood methods p.19/36

Likelihoods and odds ratios Bayes Theorem relates prior and posterior probabilities of an hypothesis H: Prob (H D) = Prob (H and D)/ Prob (D) = Prob (D H) Prob (H)/ Prob (D) The ratios of posterior probabilities of two hypotheses, 1 and 2 can be written, putting this into its odds ratio" form () cancels): Prob (H 1 D) Prob (H 2 D) = Prob (D H 1) Prob (D H 2 ) Prob (H 1 ) Prob (H 2 ) Note that this says that the posterior odds in favor of 1 over 2 are the product of prior odds and a likelihood ratio. The likelihood of the hypothesis H is the probability of the observed data given it, ). This is not the same as the probability of the hypothesis given the data. That is the posterior probability of H and requires that we also have a believable prior probability ) Lecture 4. Models of DNA and protein change. Likelihood methods p.20/36

Rationale of likelihood inference If the data consists of n items that are conditionally independent given the hypothesis i, Prob (D H i ) = Prob (D (1) H i ) Prob (D (2) H i )... Prob (D (n) H i ). and we can then write the likelihood ratio as a product of ratios: Prob (D H 1 ) Prob (D H 2 ) = ( n i=1 ) Prob (D (i) H 1 ) Prob (D (i) H 2 ) If the amount of data is large the likelihood ratio terms will dominate and push the result towards the correct hypothesis. This can console us somewhat for the lack of a believable prior. Lecture 4. Models of DNA and protein change. Likelihood methods p.21/36

Properties of likelihood inference Likeihood inference has (usually) properties of Consistency. As the number of data items n gets large, we converge to the correct hypothesis with probability 1. Efficiency. Asymptotically, the likelihood estimate has the smallest possible variance (it need not be best for any finite number n of data points). Lecture 4. Models of DNA and protein change. Likelihood methods p.22/36

A simple example coin tossing If we toss a coin which has heads probability p and get HHTTHTHHTTT the likelihood is L = Prob (D p) = pp(1 p)(1 p)p(1 p)pp(1 p)(1 p)(1 p) = p 5 (1 p) 6 so that trying to maximize it we get dl dp = 5p4 (1 p) 6 6p 5 (1 p) 5 Lecture 4. Models of DNA and protein change. Likelihood methods p.23/36

finding the ML estimate and searching for a value of p for which the slope is zero: which has roots at 0, 1, and 1 dl dp = p4 (1 p) 5 (5(1 p) 6p) = 0 Lecture 4. Models of DNA and protein change. Likelihood methods p.24/36

Log likelihoods Alternatively, we could maximize not L but its logarithm. This turns products into sums: ln L = 5 ln p + 6 ln(1 p) whereby d(ln L) dp = 5 p 6 (1 p) = 0 so that finally p = 5/11 Lecture 4. Models of DNA and protein change. Likelihood methods p.25/36

Likelihood curve for coin tosses Likelihood 0.0 0.2 0.4 0.6 0.8 1.0 p 0.454 Lecture 4. Models of DNA and protein change. Likelihood methods p.26/36

Likelihood on trees A C C G t 1 t 2 y C t 3 t 4 t 5 w t t 6 z 7 x t 8 A tree, with branch lengths, and the data at a single site This example is used to describe calculation of the likelihood Since the sites evolve independently on the same tree, L = Prob (D T) = m Prob ( ) D (i) T i=1 Lecture 4. Models of DNA and protein change. Likelihood methods p.27/36

Likelihood at one site on a tree We can compute this by summing over all assignments of states x, y, z and w to the interior nodes Prob ( D (i) T ) = x y z w Prob (A, C, C, C, G, x, y, z, w T) Lecture 4. Models of DNA and protein change. Likelihood methods p.28/36

Computing the terms For each combination of states, the Markov process allows us to express it as a product of probabilities of a series of changes, with the probability that we start in state x: Prob (A, C, C, C, G, x, y, z, w T) = Prob (x) Prob (y x, t 6 ) Prob (A y, t 1 ) Prob (C y, t 2 ) Prob (z x, t 8 ) Prob (C z, t 3 ) Prob (w z, t 7 ) Prob (C w, t 4 ) Prob (G w, t 5 ) Lecture 4. Models of DNA and protein change. Likelihood methods p.29/36

Computing the terms Summing this up, there are 256 terms in this case: x y z w Prob (x) Prob (y x, t 6 ) Prob (A y, t 1 ) Prob (C y, t 2 ) Prob (z x, t 8 ) Prob (C z, t 3 ) Prob (w z, t 7 ) Prob (C w, t 4 ) Prob (G w, t 5 ) Lecture 4. Models of DNA and protein change. Likelihood methods p.30/36

References (models) Barry, D., and J. A. Hartigan. 1987. Statistical analysis of hominoid molecular evolution. Statistical Science 2: 191-210. [Early use of full 12-parameter model] Dayhoff, M. O. and R. V. Eck. 1968. Atlas of Protein Sequence and Structure 1967-1968. National Biomedical Research Foundation, Silver Spring, Maryland. [[Dayhoff s PAM modelfor proteins] Goldman, N., and Z. Yang. 1994. A codon-based model of nucleotide substitution for protein-coding DNA sequences. Molecular Biology and Evolution 11: 725-736. [[codon-based protein/dna models] Hasegawa, M., H. Kishino, and T. Yano. 1985. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. Journal of Molecular Evolution 22: 160-174. [[HKY model] Henikoff, S. and J. G. Henikoff. 1992. Amino acid substitution matrices from protein blocks. Proceedings of the National Academy of Sciences, USA 89: 10915-10919. [[BLOSUM protein model] Lecture 4. Models of DNA and protein change. Likelihood methods p.31/36

References (models) Jones, D. T., W. R. Taylor, and J. M. Thornton. 1992. The rapid generation of mutation data matrices from protein sequences. Computer Applcations in the Biosciences (CABIOS) 8: 275-282. [[JTT model for proteins] Jones, D. T., W. R. Taylor, and J. M. Thornton. 1994a. A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry 33: 3038-3049. [JTT membrane protein model] Jones, D. T., W. R. Taylor, and J. M. Thornton. 1994b. A mutation data matrix for transmembrane proteins. FEBS Letters 339: 269-275. [[JTT membrane protein model] Jukes, T. H. and C. Cantor. 1969. Evolution of protein molecules. pp. 21-132 in Mammalian Protein Metabolism, ed. M. N. Munro. Academic Press, New York. [[Jukes-Cantor model] Kimura, M. 1980. A simple model for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16: 111-120. [[Kimura s 2-parameter model] Lecture 4. Models of DNA and protein change. Likelihood methods p.32/36

References (models) Koshi, J. M. and R. A. Goldstein. 1995. Context-dependent optimal substitution matrices. Protein Engineering 8: 641-645. [[generating other kinds of protein model matrices] Lanave, C., G. Preparata, C. Saccone, and G. Serio. 1984. A new method for calculating evolutionary substitution rates. Journal of Molecular Evolution 20: 86-93. [[General reversible model] Lockhart, P. J., M. A. Steel, M. D. Hendy, and D. Penny. 1994. Recovering evolutionary trees under a more realistic model of sequence evolution. Molecular Biology and Evolution 11: 605-612. [[The LogDet distance for correcting for changing base composition] Tamura, K. and M. Nei. 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Molecular Biology and Evolution 10: 512-526. [[Tamura-Nei model] Lecture 4. Models of DNA and protein change. Likelihood methods p.33/36

References (likelihood and Bayesian) Barry, D., and J. A. Hartigan. 1987. Statistical analysis of hominoid molecular evolution. Statistical Science 2: 191-210. [[ML with full 12-parameter model, estimated on each branch] Edwards, A. W. F., and L. L. Cavalli-Sforza. 1964. Reconstruction of evolutionary trees. pp. 67-76 in Phenetic and Phylogenetic Classification, ed. V. H. Heywood and J. McNeill. Systematics Association Publ. No. 6, London. [[first paper on likelihood for phylogenies] Felsenstein, J. 1981. Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution 17: 368-376. [[Made likelihood practical for n species] Felsenstein, J. 1973. Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Systematic Zoology 22: 240-249. [[The pruning" algorithm] Fisher, R. A. 1912. On an absolute criterion for fitting frequency curves. Messenger of Mathematics 41: 155-160. [[First modern paper introducing likelihood] Fisher, R. A. 1922. On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, A 222: 309-368. [[Likelihood in generality] Lecture 4. Models of DNA and protein change. Likelihood methods p.34/36

References (likelihood and Bayesian) Kashyap, R. L., and S. Subas. 1974. Statistical estimation of parameters in a phylogenetic tree using a dynamic model of the substitutional process. Journal of Theoretical Biology 47: 75-101. [[Second paper applying likelihood to molecular sequences] Neyman, J. 1971. Molecular studies of evolution: a source of novel statistical problems. pp. 1-27 in Statistical Decision Theory and Related Topics, ed. S. S. Gupta and J. Yackel. Academic Press, New York. [[First application of likelihood to molecular sequences] Lecture 4. Models of DNA and protein change. Likelihood methods p.35/36

How it was done This projection produced using the prosper style in LaTeX, using Latex to make a.dvi file, using dvips to turn this into a Postscript file, using ps2pdf to make it into a PDF file, and displaying the slides in Adobe Acrobat Reader. Result: nice slides using freeware. Lecture 4. Models of DNA and protein change. Likelihood methods p.36/36