codon substitution models and the analysis of natural selection pressure
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1 codon substitution models and the analysis of natural selection pressure Joseph P. Bielawski Department of Biology Department of Mathematics & Statistics Dalhousie University introduction morphological adaptation 1
2 protein structure introduction Troponin C: fast skeletal Troponin C: cardiac and slow skeletal gene sequences introduction human cow rabbit rat opossum GTG CTG TCT CCT GCC GAC AAG ACC AAC GTC AAG GCC GCC TGG GGC AAG GTT GGC GCG CAC G.C T...GC A.. A.. A.T.AA A.C AGC G.A.AT A.. AA. TG...G A...GC..G GA. C....G AT...G.GC GCT GGC GAG TAT GGT GCG GAG GCC CTG GAG AGG ATG TTC CTG TCC TTC CCC ACC ACC AAG.CT AG..G. G.. T.. GG..G..A. C.. GCT G...CC.CA.CC.CC ACC TAC TTC CCG CAC TTC GAC CTG AGC CAC GGC TCT GCC CAG GTT AAG GGC CAC GGC AAG..G G.. T.C.C..AG A.C.C. T.T A.T G.A.C..CT TC..C. A.C C.. 2
3 introduction Powerful analytical tools: 1. Population genetic data 2. Comparative analysis of codon sequences 3. Comparative analysis of amino acid sequences there is no single statistic which is best for testing the most general departures from neutrality! (Watterson 1977) overview introduction 1. introduction to modeling codon evolution 2. model based inference 3. PAML introduction & real data exercises 3
4 outline part I 1. introduction to the ω ratio 2. markov model of codon evolution 3. codon models for ω variation over branches & sites 4. model realism vs. model complexity an index of natural selection pressure 1. the ω ratio Kimura (1968) d S : number of synonymous substitutions per synonymous site (K S ) d N : number of nonsynonymous substitutions per nonsynonymous site (K A ) ω : the ratio d N /d S ; it measures selection at the protein level The genetic code determines how random changes to the gene brought about by the process of mutation will impact the function of the encoded protein. 4
5 index of natural selection pressure: ω ratio 1. the ω ratio rate ratio mode example ω < 1 purifying (negative) selection histones ω =1 Neutral Evolution pseudogenes ω > 1 Diversifying (positive) selection MHC, Lysin the basics 1. the ω ratio Why use d N and d S? (Why not use raw counts?) Example of counts: 300 codon gene from a pair of species 5 synonymous differences 5 nonsynonymous differences 5/5 = 1 Why don t we conclude that rates are equal (i.e., neutral evolution)? 5
6 the basics 1. the ω ratio Relative proportion of different types of mutations in hypothetical protein coding sequence. Expected number of changes (proportion) Type All 3 Positions 1 st positions 2 nd positions 3 rd positions Total mutations 549 (100) 183 (100) 183 (100) 183 (100) Synonymous 134 (25) 8 (4) 0 (0) 126 (69) Nonsyonymous 392 (71) 166 (91) 176 (96) 57 (27) nonsense 23 (4) 9 (5) 7 (4) 7 (4) Modified from Li and Graur (1991). Note that we assume a hypothetical model where all codons are used equally and that all types of point mutations are equally likely. the basics 1. the ω ratio Why use d N and d S? Same example, but using d N and d S : Synonymous sites = 25.5% S = % = Nonsynonymous sites = 74.5% N = % = So, d S = 5/229.5 = d N = 5/670.5 = d N /d S (ω) = 0.34, purifying selection!!! 6
7 mutational opportunity 1. the ω ratio Relative proportion of different types of mutations in hypothetical protein coding sequence. Expected number of changes (proportion) Type All 3 Positions 1 st positions 2 nd positions 3 rd positions Total mutations 549 (100) 183 (100) 183 (100) 183 (100) Synonymous 134 (25) 8 (4) 0 (0) 126 (69) Nonsyonymous 392 (71) 166 (91) 176 (96) 57 (27) nonsense 23 (4) 9 (5) 7 (4) 7 (4) Modified from Li and Graur (1991). Note that we assume a hypothetical model where all codons are used equally and that all types of point mutations are equally likely. Note: by framing the counting of sites in this way we are using a mutational opportunity definition of the sites Not everyone agrees that this is the best approach. For an alternative view see Bierne and Eyre-Walker 2003 Genetics 168: real data have biases (Drosophila GstD1 gene) 1. the ω ratio transitions vs. transversions: A G ts/tv = 2.71 C T preferred vs. un-preferred codons: Partial codon usage table for the GstD gene of Drosophila Phe F TTT 0 Ser S TCT 0 Tyr Y TAT 1 Cys C TGT 0 TTC 27 TCC 15 TAC 22 TGC 6 Leu L TTA 0 TCA 0 *** * TAA 0 *** * TGA 0 TTG 1 TCG 1 TAG 0 Trp W TGG Leu L CTT 2 Pro P CCT 1 His H CAT 0 Arg R CGT 1 CTC 2 CCC 15 CAC 4 CGC 7 CTA 0 CCA 3 Gln Q CAA 0 CGA 0 CTG 29 CCG 1 CAG 14 CGG
8 1. the ω ratio corrections and estimation bias in d S 4 3 low codon bias 4 3 med codon bias true simple model 2 d S d S 2 ts/tv + codon bias d S t high codon bias t d S t extreme codon bias t Data from: Dunn, Bielawski, and Yang (2001) Genetics, 157: x 1 x 2! x 3 x 4 t 1 t 2 t 3 j t 4 t 0 k Markov models of codon evolution A G C T 1. assumptions are explicit 2. corrections are not ad hoc 3. explicit use of a phylogeny improves power 4. principled framework for modelling and inference of the biology Goldman & Yang 1994 MBE 11: Muse & Gaut 1994 MBE 11:
9 GY-style codon models (mechanistic) some important parameters: o transition/transversion rate ratio: κ o biased codon usage: π j for codon j o nonsynonymous/synonymous rate ratio: ω = d N /d S GY-style codon models (mechanistic) let s model a change to CTG Synonymous CTC (Leu) CTG (Leu): π CTG TTC (Leu) CTG (Leu): κπ CTG Nonsynonymous GTG (Val) CTG (Leu): ωπ CTG CCG (Pro) CTG (Leu): κωπ CTG 9
10 GY-style codon models (mechanistic) From codon below: TTT (Phe) TTC (Phe) TTA (Leu) to codon below: TTG (Leu) CTT (Leu) CTC (Leu) TTT (Phe) κπ TTC ωπ TTA ωπ TTG ωκπ TTT 0 0 TTC (Phe) κπ TTT ωπ TTA ωπ TTG 0 ωκπ CTC 0 TTA (Leu) ωπ TTT ωπ TTC TTG (Leu) ωπ TTT ωπ TTC κπ TTA CTT (Leu) ωκπ TTT κπ CTC 0 CTC (Leu) 0 ωκπ TTC 0 0 κπ TTT 0 GGG (Gly) GGG (Gly) * This is equivalent to the codon model of Goldman and Yang (1994). Parameter ω is the ratio d N /d S, κ is the transition/transversion rate ratio, and π i is the equilibrium frequency of the target codon (i). P(t) = {p ij (t)} = e Qt GY-style codon models (mechanistic) (Goldman & Yang 1994 MBE 11: ) q ij 0 π j, = κπ j, ωπ j, ωκπ j, if i and j differ at 2 or 3 positions for syn.transversion for syn.transition for nonsyn. transversion for nonsyn. transition P(t) = {p ij (t)} = e Qt 10
11 likelihood of the data at a site (only two codons) CCC CCT t 0 t 1 k ( t ) p ( ) Lh ( CCC, CCT ) = π k pkccc 0 kcct t1 k Note: analysis is typically done by using an unrooted tree likelihood of the data at all sites The likelihood of observing the entire sequence alignment is the product of the probabilities at each site. N L = L 1 L 2 L 3 L N = h= 1 L h The log likelihood is a sum over all sites. l = ln{l} = ln{l 1 } + ln{l 2 } + ln{l 3 } + + ln{l N } = h= N 1 ln{ L h } 11
12 we made some progress the good: we now have a framework for o avoiding ad hoc corrections of counting methods o computation of transition probabilities * o principled framework for statistical inference a new issue: averaging ω over a pair of sequences has very low power to detect positive selection if the question is about when or where ω > 1! * Computation of transition probabilities accomplishes, in just one step, (1) a proper correction for multiple substitutions, (2) weighting for alternative pathways between codons and (3) is the basis for estimating the values of the model parameters from the data in hand. selection pressure (ω) varies in time mya mya 35 mya mya ε γ G γ A δ β Chrom. 11 β globin gene cluster Our question: When? 12
13 selection pressure (ω) varies in time mya mya mya 35 mya Chrom. 11 ε γ G γ A δ β β globin gene cluster b.l. (my) Fraction of t ω (Tot = 565my) (Tot = 1) Grey branches: ω = 0.2 Black branches: ω = 0.5 Blue branches: ω = 1.2 If we average over the tree, we do NOT detect positive selection; ω = selection pressure (ω) varies among sites Our question: Where? 13
14 selection pressure (ω) varies among sites If we average over sites, we do NOT detect positive selection; ω = 0.31 ATG. TAA Purifying: d N /d S = 0.01 Neutral: d N /d S = 1 Adaptive: d N /d S = 2 likelihood of a phylogeny problem: averaging ω over a pair has very low power if the questions are about when or where! solution: phylogenetic estimation of selection pressure x 1 x 2! x 3 x 4 t 1 t 2 j t 3 t 4 t 0 k 14
15 likelihood of a phylogeny x 1 t 1 t 2 t 0 t x 3 3 t 4 x 2 x 4 x 1 x 2! x 3 x 4 t 1 t 2 j t 3 t 4 Sum over all possible codons at ancestral nodes {TTT, TTC,, GGG} t 0 k L ) ( xh) k kx4 4 kx3 3 kj 0 jx2 2 jx1 1 k j = [ π p ( t ) p ( t ) p ( t ) p ( t ) p ( t ] improvements. 3. ω variation x 1 x 2 x 3 x 4 t 1 :ω 1 t 2 :ω 1 t 3 :ω 0 t 4 :ω 0 j t 0 :ω 0 k ω 1 ω 0 ω 1 ω 0 ω 1 GTG CTG TCT CCT GCC GAC AAG ACC AAC GTC AAG GCC GCC TGG GGC AAG GTT G.C T.. A.. A.T.AA A.C G.A.AT A.. AA. TG...G A....G GA. C....G AT...G 3.1. branch models (ω varies among branches) 3.2. site models (ω varies among sites) 15
16 3. ω variation 3.1. branch models * x 1 x 2 x 3 x 4 t 1 :ω 1 t 2 :ω 1 j t 3 :ω 0 t 4 :ω 0 t 0 :ω 0 k Variation (ω) among branches: Yang, 1998 Bielawski and Yang, 2003 Seo et al Kosakovsky Pond and Frost, 2005 Dutheil et al Approach fixed effects fixed effects auto-correlated rates genetic algorithm clustering algorithm * These methods can be useful when selection pressure is strongly episodic 3.1. branch models 3. ω variation x 1 x 2 x 3 x 4 t 1 :ω 1 t 2 :ω 1 j t 3 :ω 0 t 4 :ω 0 t 0 :ω 0 o species colonization of a new niche k o altered context for gene expression o gene duplication event(s) o lateral gene transfers (LGTs) o cross-species virus transmission & host switching o organismal adaptive radiations 16
17 3. ω variation 3.2. site models * GTG CTG TCT CCT GCC GAC AAG ACC AAC GTC AAG GCC GCC TGG GGC AAG GTT GGC GCG CAC G.C T...GC A.. A.. A.T.AA A.C AGC G.A.AT A.. AA. TG...G A...GC..G GA. C....G AT...G.GC Variation (ω) among sites: Yang and Swanson, 2002 Bao, Gu and Bielawski, 2006 Massingham and Goldman, 2005 Kosakovsky Pond and Frost, 2005 Nielsen and Yang, 1998 Kosakovsky Pond, Frost and Muse, 2005 Huelsenbeck and Dyer, 2004; Huelsenbeck et al Rubenstein et al Bao, Gu, Dunn and Bielawski 2008 & 2011 Murell et al Approach fixed effects (ML) fixed effects (ML) site wise (LRT) site wise (LRT) mixture model (ML) mixture model (ML) mixture (Bayesian) mixture model (ML) mixture (LiBaC/MBC) mixture (Bayesian) Useful when at some sites evolve under diversifying selection pressure over long periods of time This is not a comprehensive list site models: M-series 3. ω variation Model Code NP Parameters One-ratio M0 1 ω Neutral M1a 2 P 0, ω 0, Selection M2a 4 p 0, p 1, ω 0, ω 2 Discrete M3 2K-1 p 0, p 1,, p K-2 ω 0, ω 1,, ω K-2 Frequency M4 5 p 0, p 1,, p 4 Gamma M5 2 α, β 2Gamma M6 4 p 0, α 0, β 0, α 1 Beta M7 2 p, q Beta&ω M8 4 p 0, p, q, ω Beta&gamma M9 5 p 0, p, q, α, β Beta&normal+1 M10 5 p 0, p, q, α, β Beta&normal>1 M11 5 p 0, p, q, μ, α 0&2normal>1 M12 5 p 0, p 1, μ 2, α 1, α 2 3normal>0 M13 6 p 0, p 1, μ 2, α 0, α 1, α 2 17
18 discrete model K 1 human cow rabbit rat opossum 3. ω variation GTG CTG TCT..G CCT G.C G.A GA. GCC.AT GAC AAG ACC T.. AAC C.. GTC A.. A....G AAG GCC A.T AA. GCC TG. AT. TGG GGC.AA..G AAG GTT A.C A....G GGC GCG.GC AGC.GC.GC CAC A.. GCT.G..G. GGC GAG.CT.CC TAT GGT.CA GCG.A. GAG GCC GAG C...CC AGG ATG.CC TTC CTG T.. GCT TCC AG. GG. G.. TTC CCC ACC ACC AAG ACC TAC TTC CCG T.T CAC TTC A.T GAC CTG T.C G.A CTG G.. 1 AGC C TC CAC.C..C. GGC TCT..G GCC.AG CAG GTT A.C A.C AAG C.. GGC.C..CT CAC GGC AAG G.. P(x h ) = pi P(x h ωi ) i =0 ω = 2.0 ω = 0.01 ω = site models globin model directional selection human cow rabbit rat opossum 3. ω variation GTG CTG TCT..G CCT G.C G.A GA. GCC.AT GAC AAG ACC T.. AAC C.. GTC A.. A....G AAG GCC A.T AA. GCC TG. AT. TGG GGC.AA..G AAG GTT A.C A....G GGC GCG.GC AGC.GC.GC CAC A.. GCT.G..G. GGC GAG.CT.CC TAT GGT.CA GCG.A. GAG GCC CTG G.. GAG C...CC AGG ATG.CC TTC CTG T.. GCT TCC AG. GG. G.. TTC CCC ACC ACC AAG ACC TAC TTC CCG T.T CAC TTC A.T GAC CTG T.C G.A AGC.C. TC..C..C...G.AG A.C A.C C...C..CT G.. CAC GGC TCT GCC CAG GTT AAG GGC CAC GGC AAG MHC model diversifying site models selection Site models detect diversifying positive selection; absence of signal under a sites model does not mean changes in a protein did not have fitness consequences (Bielawski et al PNAS 101: ) 18
19 3. ω variation 3.2. site models GTG CTG TCT CCT GCC GAC AAG ACC AAC GTC AAG GCC GCC TGG GGC AAG GTT GGC GCG CAC G.C T...GC A.. A.. A.T.AA A.C AGC G.A.AT A.. AA. TG...G A...GC..G GA. C....G AT...G.GC o genetic incompatibilities in human infertility o non-hormonal contraception drugs o identify pathogenicity genes o venom-anti-venom co-evolution o identify candidate genes for drug therapies o identify immune and defense system genes o vaccine design o aid functional classification of unknown genes o incorporate in models of protein 2D and 3D structure we made some more progress. 3. ω variation x 1 x 2 x 3 x 4 t 1 :ω 1 t 2 :ω 1 t 3 :ω 0 t 4 :ω 0 j t 0 :ω 0 k ω 1 ω 0 ω 1 ω 0 ω 1 GTG CTG TCT CCT GCC GAC AAG ACC AAC GTC AAG GCC GCC TGG GGC AAG GTT G.C T.. A.. A.T.AA A.C G.A.AT A.. AA. TG...G A....G GA. C....G AT...G 3.1. branch models (ω varies among branches) 3.2. site models (ω varies among sites) 3.3. branch-site models (combines the features of above models) 19
20 3.3. branch-site model: model B 3. ω variation P( x h ) = K 1 i= 0 p P( x i h ω ) i Foreground branch only ω ω ω = 0.01 = 0.90 = 5.55 ω for background branches are from site-classes 1 and 2 (0.01 or 0.90) 3.3. models for variation in branches & sites 3. ω variation Variation (ω) among branches & sites: Yang and Nielsen, 2002 Forsberg and Christiansen, 2003 Bielawski and Yang, 2004 Giundon et al., 2004 Zhang et al Kosakovsky Pond et al. 2011, 2012 Approach fixed+mixture (ML) fixed+mixture (ML) fixed+mixture (ML) switching (ML) fixed+mixture (ML) full mixture (ML) * These methods can be useful when selection pressures change over time at just a fraction of sites * It can be a challenge to apply these methods properly (more abut this later). 20
21 a MODEL is an intentional simplification of a complex situation designed to eliminate extraneous detail in order to focus attention on the essentials of the situation! (Daniel L. Hartl) (images from Paul Lewis) there is a kind of tension between model realism and model complexity UNDER FIT OVER FIT bias total error total error variance error (low) model complexity (high) 21
22 AA exchangeabilties 4. realism vs. complexity intentional simplification: all of the models listed above treat these two amino acid substitutions in the same way! 1. ATA (Ile) è TTA (Leu): ωπ TTA (conservative) 2. ATA (Ile) è AAA (Lys): ωπ AAA (radical) Q matrix for M0 codon models 4. realism vs. complexity From codon below: TTT (Phe) TTC (Phe) TTA (Leu) to codon below: TTG (Leu) CTT (Leu) CTC (Leu) TTT (Phe) κπ TTC ωπ TTA ωπ TTG ωκπ TTT 0 0 TTC (Phe) κπ TTT ωπ TTA ωπ TTG 0 ωκπ CTC 0 TTA (Leu) ωπ TTT ωπ TTC TTG (Leu) ωπ TTT ωπ TTC κπ TTA CTT (Leu) ωκπ TTT κπ CTC 0 CTC (Leu) 0 ωκπ TTC 0 0 κπ TTT 0 GGG (Gly) GGG (Gly) * This is equivalent to the codon model of Goldman and Yang (1994). Parameter ω is the ratio d N /d S, κ is the transition/transversion rate ratio, and π i is the equilibrium frequency of the target codon (i). 22
23 COLD R matrix (log scale) 4. realism vs. complexity structure of rate matrix: GY-M0 M0 R matrix (log scale) 2 parameters (κ and ω) too sparse? Structure of a Q matrix (log scaled and binned) derived from M0 for Abolone sperm lysin isons of estimated R-matrices the R matrix on logarithm derived by scale: our selected model using F R-matrices by COLD and estimated R matrix by fitting method and the R matrix derived from M0 for structure of rate matrix: ECM ECM R matrix (log scale) 1830 parameters 4. realism vs. complexity COLD R matrix (log scale) way too al framework for codon models of phylogeny, which many! e the currently used models and provides the flexatrix to be decided by a number ECM (Kosiol et al. of 2007) related for all sequences in the factors. Pandit database Structure of a Q matrix (log scaled and binned) derived y to construct the model matrix to include some of Figure 8: Pattern comparisons of estimated R-matr tors to model the rateecm matrix. R matrix, The factors estimated that will R-matrices by COLD and M0 for Lysin data. 23 re data dependent; this is achieved through model
24 codon rate matrix 4. realism vs. complexity UNDER FIT? OVER FIT? bias total error total error variance error the R matrix derived by our selected model using F 61 as codon frequency fitting method and the R matrix derived from M0 for Lysin data in Figure 8. COLD R matrix (log scale) M0 R matrix (log scale) ECM R matrix (log scale) COLD R matrix (log scale) M0 R matrix (log scale) (low) model complexity (high) Figure 8: Pattern comparisons of estimated R-matrices on logarithm scale: ECM R matrix, estimated R-matrices by COLD and estimated R matrix by M0 for Lysin data. Figure 8: Pattern comparisons of estimated R-matrices on logarithm scale: ECM R matrix, estimated R-matrices by COLD and estimated R matrix by M0 for Lysin data. 6 Conclusion 6 Conclusion We have presented a general framework for codon models of phylogeny, which We have presented a general framework for codon models of phylogeny, which is general enough to include the currently used models and provides the flexibility to allow the rate matrix to be decided by a number of related factors. 4. realism vs. iscomplexity general enough to include the currently used models and provides the flexibility asderived codon to allow the by frequency rateour matrixselected to be decidedmodel by a number using of related Ffactors. 61 as trix derived by our selected model trying the using Rto matrix Ffind 61 as a derived balance codon by frequency our selected model the using R matrix F 61 We further proposed a way to construct the model matrix to include some of We further proposed a way to construct the model matrix to include some of thod the possibly andimportant the Rfactors matrix to model derived the rate matrix. from fitting TheM0 factorsfor method thatlysin will and datathe in RFigure matrix 8. derived fromfitting M0 for method Lysinand datathe inrfigure matrix 8. derived from M0 for Lysin the possibly important factors to model the rate matrix. The factors that will be included in the model are data dependent; this is achieved through model be included in the model are data dependent; this is achieved through model selection. Our model typically results in a richer pattern for the R matrix R matrix (log scale) COLD R matrix (log scale) ECM R M0 matrix R matrix (log (log scale) scale) COLD R matrix (log scale) ECM R M0 matrix R matrix (log (log scale) scale) COLD R matrix (log scale) M0 R m selection. Our model typically results in a richer pattern for the R matrix than the currently available codon models. The proposed model framework than the currently available codon models. The proposed model framework allows us to fit more realistic models to the data but avoids the unnecessarily allows us to fit more realistic models to the data but avoids the unnecessarily complicated models by applying model selection. complicated models by applying model selection parameters : 2 (fitted) 23 (fitted) 1830 (not fitted) lnl (Lysin) : Pattern comparisons of estimatedfigure R-matrices 8: Pattern on logarithm comparisons scale: of estimatedfigure R-matrices 8: Pattern on logarithm comparisons scale: of estimated R-matrices on atrix, estimated R-matrices by COLD ECMand R matrix, estimated estimated R matrix R-matrices by by COLD ECMand R matrix, estimated estimated R matrix R-matrices by by COLD and estim sin data. M0 forquestion: Lysin data. does this impact inference of M0 positive for Lysin selection? data. preliminary answer: sometimes (but, oftentimes no) nclusion 6 Conclusion 6 Conclusion resented a general framework for codon We have models presented of phylogeny, a general which framework for codon We have models presented of phylogeny, a general which framework for codon models of enough to include the currently used is general models enough and provides to include the flexallow the rate matrix to be decided the currently used is general modelsenough and provides to include the flexibility the currently used models and 24 by a to number allow of the related rate matrix factors. to be decidedibility by a to number allow of therelated rate matrix factors. be decided by a number r proposed a way to construct the model matrix to include some of We further proposed a way to construct thewe model further matrix proposed to include a way some to construct of the model matrix t
25 modeling amino acid exchangeabilities 4. realism vs. complexity multiple amino acid exhangeabilities: model type Yang and Goldman 1994; Yang et al. 1998; PCP Sanudiin et al. 2005; Wong et al PCP Conant and Stadler (2009) PCP Kosiol et al. 2007; De Maio et al ECM è MEP * Doron-Faigenboin & Pupko 2007 MEP * Miyazawa (2011) MEP (LCEP) * Zoller and Schneider (2012) MEP (LCEP) * Delport et al GPP (GA) Zaheri, Dib and Salamin (2014) GPP * Bielawski et al. (in prep.) GPP * PCP: physiochemcially constrained parametric model MEP: mixed empirical and parametric model ECM: empirical codon model GPP: general purpose parametric model * models permitting double and triple changes among codons lots of options codon models o methods have different pros and cons (that s OK) o researchers have different preferences (that s OK) o I use a wide variety of different methods; you might have different preferences (that s OK) o most important: KNOW YOUR DATA and MAKE INFORMED DECISIONS. no single method will be suitable for all situations 25
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