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Positive & Negative Selection Plan Codon models and positive selection in protein evolution • Positive selection & its importance • Methods for detecting positive selection • Detecting amino acid sites under positive Ziheng Yang selection Department of Biology • Genes detected to be under positive University College London selection Positive & negative selection There are two main explanations for genetic variation observed within a Genotype AA Aaaa population or between species: Frequency p2 2p(1−p) (1−p)2 Fitness 1 1+s 1+2s Natural selection (survival of the fittest) Mutation and drift (survival of the luckiest) (A: “wild-type allele”; a: new mutant) s is selection coefficient: Gillespie, J.H. 1998. Population genetics: a concise guide. John s ≈ 0: neutral evolution Hopkins University Press, Baltimore. s < 0: negative (purifying) selection Hartl, D.L., and A.G. Clark. 1997. Principles of population genetics. s > 0: positive selection (adaptive evolution) Sinauer Associates, Sunderland, Massachusetts. Theories of molecular evolution Detecting selection is useful • for testing evolutionary theory • for identifying functional elements in genomes. Akashi, H. (1999) Gene 238: 39-51 Conservation → Evolutionary conservation means function function function About 12Mb of the cystic fibrosis region were sequenced in 12 vertebrate and fish species, and used to identify a number of conserved non- Genes or genome regions conserved across coding segments previously diverse species most likely have some unknown. Closely related mammalian species are functional significance. effective in identifying regulatory elements while distantly related species are effective in identifying coding regions. (Thomas, et al. 2003. Nature 424:788-793) High variability may also mean Positive selection can be detected functional significance, using population genetics tests of if the variability is driven by neutrality selection. • McDonald & Kreitman test (1991) Evolutionary biologists are more interested in positive • Hudson, Kreitman and Aquade (HKA) test (1987) selection because fixations of advantageous mutations in the genes or genomes are responsible for • Fu & Li test (1993) evolutionary innovations and species divergences. • Fay, Wyckoff & Wu (2002) Fay JC, Wu CI. 2003. Annu. Rev. Genomics. Hum. Genet. 4:213-235. Kreitman, M. 2000. Annu. Rev. Genomics Hum. Genet. 1:539-559. Nielsen R. 2005. Annu. Rev. Genet 39:197-218. The nonsynonymous/synonymous rate Positive selection can also be ω detected through phylogenetic ratio contrasts our expectations based on the genetic code and our observations comparison of synonymous and on the genetic code and our observations after the filtering of selection on the nonsynonymous substitution rates protein. • ω = 1: neutral evolution (s = 0) • ω < 1: negative (purifying) selection (s < 0) If we expect N:S to be 74.5%:25.5% before • ω > 1: positive (diversifying) selection (s > 0) selection on the protein, and observe 5:5 substitutions (differences), then ω = dN/dS = (5/5)/(74.5%/25.5%) = 0.34 (Miyata and Yasunaga 1980; Gojobori 1983; Li et al. 1985; Nei & Gojobori 1986) Codon-substitution model: Rates to CTG Definitions Synonymous CTC (Leu) → CTG (Leu) π dS (KS) : number of synonymous substitutions CTG per synonymous site → κπ TTG (Leu) CTG (Leu) CTG dN (KA): number of nonsynonymous substitutions per nonsynonymous site Nonsynonymous ω = d /d : nonsynonymous/synonymous rate N S GTG (Val) → CTG (Leu) ωπ ratio GTG (Val) CTG (Leu) CTG → κωκ π CCG (Pro) CTG (Leu) CTG Rate matrix Q = {qij} Likelihood calculation on a tree sums over all possible codons for each ancestral node ⎧0 if i and j differ at 2 or 3 positions ⎪π , for synonymous transversion ⎪ j = ⎪κπ TTT qij ⎨ j , for synonymous transition ⎪ωπ , for nonsynonymous transversion t ⎪ j 1 ωκπ {TTT, TTC, …, GGG} ⎩⎪ j , for nonsynonymous transition P(t) = {p (t)} = eQt ij t2 t3 (Goldman & Yang 1994 Mol Biol Evol 11:725-736 Muse & Gaut 1994 Mol Biol Evol 11:715-724) TTC TTT Codon substitution models Branch models • Branch models to test positive selection on lineages on the tree (Yang 1998. Mol. Biol. Evol. 15:568-573) • Site models to test positive selection affecting individual sites (Nielsen & Yang. 1998. Genetics 148:929-936; Yang, et al. 2000. Genetics 155:431-449) • Branch-site models to detect positive selection at a few sites on a particular lineage (Yang & Nielsen. 2002. Mol. Biol. Evol. 19:908-917; Yang, et al. 2005. Mol. Biol. Evol. 22:1107-1118) Rhesus Macaque Genome Sequencing and Analysis Consortium. 2007. Evolutionary and biomedical insights from the Rhesus macaque genome. Science 316:222-234. Adaptive evolution in primate lysozyme Douc langur Log-likelihood values and Dusky langur Francois' Langur parameter estimates ω Hanuman langur Colobines C Purple-faced langur Proboscis monkey ω ω ω Guereza colobus Model p A 0 C 0 Angolan colobus Patas monkey ω ω 35 –1043.84 0.574 ω Vervet A. 1-ratio: 0= C = 0 Talapoin Cercopithecines Allen's monkey ω ω 36 –1041.70 0.489 3.383 Olive baboon B. 2-ratios: 0, C Sooty mangabey ω Rhesus macaque C. 2-ratios: ω , ω =1 35 –1042.50 0.488 1 H Lar gibbon 0 C H Human Hominoids chimpanzee, bonobo, gorilla Orangutan Squirrel monkey New World (Yang 1998 Mol. Biol. Evol. 15: 568-573 Tamarin monkeys Data from Messier & Stewart 1997 Nature 385: 151-154) Marmoset Site models Likelihood ratio test statistics Early studies average synonymous and nonsynonymous rates over sites and have little power in detecting adaptive evolution. Null hypothesis 2ΔA ω ω ω C = 0 4.24* ω 1 C = 1 1.60 0 GAA AAC AGA TTT TCA ATG CCC CAT CCT TTA AAA CAC … Possible approaches Possible approaches one ω for every site • Estimate and test one ω for every site (Fitch et al. 1997 PNAS 94:7712-7718; Suzuki & Gojobori 1999 Mol. Biol. Evol. 16: 1315-1328; Suzuki 2004 J. Mol. Evol. 59: 11-19; TTC Massingham and Goldman 2005 Genetics 169: 1753-1762; Kosakovsky Pond and Frost 2005 Mol. Biol. Evol. 22: 1208-1222) TTC TTC T→A TTC ATC • Focus on sites potentially under selection based on TTC TTA C→A structure T→A (Hughes & Nei 1988 Nature 335:167-170; Yang & Swanson 2002 TAT Mol. Biol. Evol. 19: 49-57) (fixed-sites model) C→T TTT TTT • Use a statistical distribution to model the ω variation (Nielsen & Yang 1998 Genetics 148: 929-936; Yang et al. 2000 3 nonsynonymous changes Genetics 155: 431-449) (random-sites model, fishing expedition) 1 synonymous change ω The approach of one for a site Use of codon models to detect amino uses too many parameters. acid sites under diversifying selection • Likelihood ratio test (LRT) for sites under positive selection The standard approach to dealing with the • Empirical Bayesian calculation of posterior ω problem is to assign a prior on and use a probabilities of sites under positive nonparametric or parametric empirical Bayes selection approach. Two pairs of useful models LRT of sites under positive selection M1a (neutral) Site class: 0 1 Proportion: p0 p1 ω ω ω ω H0: there are no sites at which > 1 ratio: 0<1 1=1 H1: there are such sites Δ χ2 M2a (selection) Compare 2 A = 2(A1 - A0) with a distribution Site class: 0 1 2 Proportion: p0 p1 p2 ω ω ω ω ratio: 0<1 1=1 2>1 (Nielsen & Yang 1998 Genetics 148:929-936; Yang, Nielsen, Goldman & Pedersen 2000. Genetics 155:431-449) ω Modified from Nielsen & Yang (1998), where 0=0 is fixed Human MHC Class I data: M7 (beta) ω ~ beta(p, q) 192 alleles, 270 codons Model A Parameter estimates M8 (beta&ω) − ω M1a (neutral) 7,490.99 p0 = 0.830, 0 = 0.041 p = 0.170, ω = 1 p0 of sites from beta(p, q) 1 1 p = 1 − p of sites with ω > 1 1 0 s − ω M2a (selection) 7,231.15 p0 = 0.776, 0 = 0.058 ω p1 = 0.140, 1 = 1 ω p2 = 0.084, 2 = 5.389 Likelihood ratio test of positive selection: Yang, Nielsen, Goldman, Pedersen (2000 Genetics 155:431-449) 2ΔA = 2 × 259.84 = 519.68, P < 0.000, d.f. = 2 Empirical Bayesian calculation of Naïve Empirical Bayes (NEB) posterior probabilities that a site is under positive selection with ω > 1. Under M2a, there are ω ω ω Three site classes: 0 = 0.058, 1 = 1, 2 = 5.389 • Naïve Empirical Bayes (NEB) ignores sampling Prior proportions: p = 0.776, p = 0.140, p = 0.084 errors in parameter estimates. 0 1 2 • Bayes Empirical Bayes (BEB) accounts for sampling errors by integrating over a prior. Bayes’s theorem is used to calculate the posterior probabilities for the three site classes for each site, given the data. Nielsen & Yang. 1998. Genetics 148:929-936. Yang, Wong & Nielsen. 2005. Mol. Biol. Evol. 22:1107-1118. Posterior probabilities for MHC (M2a) ω = 5.389 ω = 1 ω = 0.058 1 0.8 0.6 25 sites 0.4 0.2 identified 0 under M2a 1 0.8 0.6 0.4 0.2 0 With more genomes sequenced, the approach of Advantages of ML evolutionary comparison will become more • Accounts for the genetic code powerful. It provides a way of generating • Accounts for transition-transversion rate interesting biological hypotheses, which can be differences and codon usage validated by experimentation. • Avoids bias in ancestral reconstruction • Uses probability theory to correct for multiple hits Ivarsson, Y., A. J. Mackey, M. Edalat, W. R. Pearson, and B. Mannervik. 2002. Identification of residues in glutathione transferase capable of driving functional diversifcation in evolution: a novel approach to protein design. J. Biol. Chem. 278:8733-8738. Disadvantages of ML Bielawski, J. P., K. A. Dunn, G. Sabehi, and O. Beja. 2004. Darwinian • Model assumptions may be unrealistic. adaptation of proteorhodopsin to different light intensities in the marine • The method detects positive selection only if it environment.
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