Natural selection on the molecular level

Transcription

1 Natural selection on the molecular level

2 Fundamentals of molecular evolution How DNA and protein sequences evolve?

3 Genetic variability in evolution } Mutations } forming novel alleles } Inversions } change gene order } may impede recombinatios and fix a haplotype } Duplications } may involve genes or gene fragments } or entire chromosomes and genomes } the main source of evolutionary innovations } Horizontal gene transfer } including symbiotic events 3

4 Substitutions } Why transitions are more frequent? } there are more possible transversions } observed ts/tv ratio from ~2 (ndna) to ~15 (human mtdna) } exception plant mtdna } the ts/tv ratio varies significantly between lineages 4

5 Transitions and transversions } Why are the transitions more frequent? } Selection hypothesis (transitions are more likely to be silent and neutral) } But: } transitions are more frequent in rrna genes, pseudogees and noncoding regions } transitions are more frequent in 4-fold degenerate positions (codons like CUN Leu) 5

6 Transitions and transversions } Why are the transitions more frequent? } Mechanistic explanation mutagenesis and repair mechanisms } Transitions are the result of: } tautomeric shift of bases } deamination (e.g. oxidative) } Transitions cause less distortion of the double helix structure } less likely to be detected and repaired by the MMR system 6

7 Modeling DNA evolution } Ancestral sequence usually not available } The number of mutations has to be inferred from differences between present sequences } Requires correcting for multiple hits, particularly for more distant sequences 7

8 Modeling sequence evolution } Markov models the state of generation n +1 depends only on the state of generation n and the rule set (character substitution probability matrix) } There are many models with varying complexity } Parameters may include: } multiple hits (Poisson correction) } different substitution probabilities for various mutations } different substitution probabilities for various ositions in sequence } nucleotide frequencies 8

9 DNA evolution -the simplest model (Jukes- Cantor) A C G T A 1-3α α α α C α 1-3α α α G α α 1-3α α T α α α 1-3α } } } } equal nucleotide frequencies (f=0.25) equal substitution probabilities corrected distance: D JC = 3 4 ln(1 4 3 D) Example: D OBS =0.43 => D JC =0.64 9

10 Other models } Kimura (K80, two-parameter) - different probabilities for transitions and transversions } Felsenstein (F81), Hasegawa-Kishino-Yano (HKY85) - different nucleotide frequencies (F81) + different probabilities for transitions and transversions (HKY85) } GTR (General Time Reversible, Tavare 86) 10

11 The GTR model } Different probabilities for each substitution (but symmetrical, e.g. A T = T A) - 6 parameters } Different nucleotide frequencies - 4 parameters 11

12 The gamma distribution } In simple models each position in sequence has the same substitution probability - unrealistic } Different classes of substitution probabilities are modeled using the gamma distribution 12

13 On the level of the genetic code } A mutation can: } change the codon to another codon encoding the same aa } synonymous } change the codon to a codon encoding a different aa } nonsynonymous } change the codon to a STOP codon } nonsens } cause a frameshift } change gene expression 13

14 Mutations and the natural selection } We do not observe mutations } we observe differences between populations (species) } or intra-population variation (polymorphism) } Alleles formed by mutation are subject to selection } Allele frequencies can be changed by genetic drift } We observe mutations that are fixed entirely or partially (polymorphisms) in the gene pool 14

15 The fundamental question of molecular evolution } What is the role of genetic drift and natural selection in shaping sequence variation? } intrapopulation (polymorphisms) } interspecific } The question concerns quantitative differences } There is no doubt that evolutionary adaptations are a result of natural selection } But, how many of the differences we observe are adaptive? } And which ones? 15

16 Selection or drift? } Selectionism } the majority of fixed mutations were positively selected } most polymorphisms are maintained by selection } balancing selection, overdominance, frequency-based selection } Neutralism (Kimura, 1968) } the majority of fixed mutations were fixed by genetic drift (by chance) } the majority of polymorphisms are the result of drift } positively selected mutations are rare and are not significant for the quantitative analysis of molecular variation 16

17 Mutations and natural selection } deleterious } s < 0 } eliminated by selection (purifying/negative) } neutral } s 0 (more precisely, s 1/4N) } can be fixed or lost by genetic drift } beneficial } s > 0 } fixed by positive selection (influenced by drift for small s) 17

18 Selectionism vs neutralism } Selectionism: } most mutations are deleterious } most fixed mutations are beneficial } neutral mutations are rare (not more frequent than beneficial) } Neutralism } most mutations are deleterious or neutral } most fixed mutations are neutral } beneficial mutations are rare (much less frequent than neutral), but still important for adaptation 18

19 Selectionism vs neutralism selectionism neutralism pan-neutralism Neutralism is not pan-neutralism, nobody denies the selective importance of mutations 19

20 Neutral theory - indications } The rate of substitution and the degree of polymorphism are too high to be explained by selection alone } Constant rate of molecular evolution (molecular clock) } Less important sequences (pseudogenes, less important fragments of proteins) change faster than key functional sequences 20

21 The constant rate of molecular evolution } Many sequences evolve at a constant rate } The rate varies for different sequences, but for the same sequence remains constant between lineages } Tzw. zegar molekularny Amino acid differences for vertebrate globins 21

22 Drift and the rate of evolution } Genetic drift is a random process, but its rate is equal over long time } Depends only on mutation rate (one fixed change per 1/µ generations) } For selection a constant rate only if the environment changes at a constant rate (unlikely) } The rate of adaptive changes is not constant 22

23 Molecular clock } A consequence of the neutral model } Sequences diverge at a constant rate for a given gene } different rates for different genes } Relative rate test K AC - K BC = 0 A B C } tests for constant rate between lineages, but not ove time 23

24 Molecular clock - the generation problem } In the neutral model the fixation rate is: } Should be constant per generation } Different organisms have different generation times } The rate of evolution should not be constant over time 1 2Nµ 2N = µ } But that s what was observed 24

25 The constant rate of molecular evolution } Tzw. zegar molekularny Amino acid differences for vertebrate globins 25

26 The generation problem } Generation time varies among different organisms ~0,03 generations/year ~3 generations/year } Why does the evolution rate remain constant? 26

27 The near-neutral model } The original neutral model concerns purey neutral changes (s = 0), which are rare } Mutations behave like neutral if: s 1 4N e } Mutations with a small selection coefficient s will behave like neutral mutations in small populations } in large populations they will be subject to selection 27

28 Near-neutral model } There is a negative correlation between generation time and population size 28

29 The near neutral model ~0,03 generations/year s 1 4N e ~3 generations/year Long generation time Less mutations per year Short generation time More mutations per year Small population (small N e ) Large population (large N e ) More mutations behave like neutral and are fixed by drift More mutations are subject to selection Generation time and population size cancel each other, resulting in a constant rate (Ohta & Kimura, 1971). 29

30 The molecular clock } Rate constant over time for proteins and nonsynonymous substitutions } In DNA, } for synonymous mutations } pseudogenes } some noncoding sequences } the rate depends on the generation time 30

31 Eveolutionary rate and function Less important sequences (pseudogenes, less important fragments of proteins) change faster than key functional sequences 31

32 Degeneracy (redundancy) in the code 32 4-fold degenerate 2-fold degenerate

33 Eveolutionary rate and function Less important sequences (pseudogenes, less important fragments of proteins) change faster than key functional sequences 33

34 Evolutionary rate unit: PAM/10 8 years Evolutionary unit time: how long (in millions of years, 10 6 ) does it take to fix 1 mutation/100 aa (1 PAM) 34

35 Evolutionary rate } Negative (purifying selection) is the main factor influencing the rate of evolution } in more important sequences a mutation is more likely to be deleterious (and eliminated by selection) } in more important sequences a mutation is more likely to be neutral (and may be fixed by drift) } mutations that don t influence function will be neutral } pseudogenes } noncoding regions? } synonymous substitutions? 35

36 Neutralism - the current status } A foundation that explains many observations } high DNA and protein polymorphism } } molecular clock } many deviations, there is no global clock, local clocks can be found slower evolution of more important sequences } It is a null hypothesis for testing for the positive selection at the molecular level 36

37 Neutralism - the current status } verified by sequencing, currently on the genomic scale } Kimura: 1968 before DNA sequencing was invented 37

38 Neutralism - the current status } Smith & Eyre-Walker % amino acid substitutions in Drosophila sp. fixed by selection } Andolfatto 2005 between D. melanogaster and D. simulans postive selection responsible for: } 20% DNA substitutions in introns and intragenic sequences } 60% DNA substitutions in UTR sequences 38

39 Neutralism - the current status } The main achievement of the neutral theory is the development of a mathematical frameworkto sudy the effects of selection and drift } Allowed to develop methods of testing for positive selection (using the neutral model as a null hypothesis) } A significant portion of the genome evolves according to the neutral model } a molecular clock can be found 39

40 40 Testing selection

41 Looking for traces of selection } Most sequences evolve in a constant, clocklike fashion } Deviation from the constant rate in a given lineage lineage-specific selection Constant rate Accelerated changes Slowed changes

42 Testing for selection } Assumption: synonymous mutations are neutral } For pairwise comparisons, calculate Ka (dn) number of nonsynonymous changes per number of nonsynonymous sites Ks (ds) number of synonymous changes per number of synonymous sites The rate Ka/Ks (ω) reflects selection ω=1 purely neutraln ω<1 negative (purifying) selection ω>1 positive selection

43 Testing for selection } The ω rate is rarely larger than 1 globally for the entire gene (exceptions, e.g. MHC genes) } Average ω between primates and rodents is 0.2, between human and chimp: 0.4 } Deviations from average ω for a given gene in a given lineage indicate selection } In a gene there can be sites with different ω, indicating selection acting on particular regions of the sequence

44 Testing for selection II } Comparing synonymous and nonsynonymous rates for intraspecific and interspecific comparisons

45 The McDonald-Kreitman test } Comparing synonymous and nonsynonymous rates for intraspecific and interspecific comparisons } In a neutral model both rates should be equal

46 Example: the ADH gene in 3 Drosophila species synonymous nonsynonymous rate intraspecific 42 2 ~0.05 interspecific 17 7 ~0.41 Conclusion: nonsynonymous changes favoured during speciation - not neutral

47 Comparing hypotheses } The likelihood criterion } PAML (Phylogenetic Analysis by Maximum Likelihood) author Ziheng Yang }

48 Likelihood } } } Likelihood = the probability of obtaining the observed data under a given model and its parameters: P(data model) Probability relates to future events in a known model - e.g. for a symmetrical coin toss, probability of heads = 1/2 (two heads 1/4 etc.) Likelihood explains observations (past) with an unknown model - e.g. observing 50% heads and 50% tails is most likely for a symmetrical coin toss

49 The likelihood ratio test } LRT (Likelihood Ratio Test) } Comparing nested models with different parameter count } For each model calculate likelihoods (L1, L2) LR = 2 [ln(l1) ln(l2)] } The significance of LR is determined by comparing to χ2 for df (degrees of freedom) equal to the difference in parameter count

50 Example #1 Model 0 ω the same for the whole tree: likelihood L0 Model 1 ω for lineage #1 different, than for the rest of the tree: likelihood L1 Is the rate of evolution different for lineage #1? LR = 2 [ln(l1) ln(l0)] Is LR larger than χ 2 for df=1?

51 Input files for PAML } Sequences: PHYLIP format (e.g. from Clustal) } Tree: Newick format, with optional branch labels } Control file: codeml.ctl

52 Parentheses (Newick) tree format (Macaca,Cerco,(Pongo,(Gorilla,(Homo,Chimp)))); (Macaca,Cerco,(Pongo,(Gorilla,(Homo#1,Chimp)))); #1 #1 #1 #1 #1 #1 (Macaca,Cerco,(Pongo,(Gorilla,(Homo,Chimp))$1)); #1 #1 #1 #2 #1 (Macaca,Cerco,(Pongo,(Gorilla,(Homo#2,Chimp))$1));

53 seqfile = treefile = outfile = noisy = 9 verbose = 2 runmode = 0 * 0,1,2,3,9: how much rubbish on the screen * 1: detailed output, 0: concise output * 0: user tree; 1: semi-automatic; 2: automatic * 3: StepwiseAddition; (4,5):PerturbationNNI seqtype = 1 * 1:codons; 2:AAs; 3:codons-->AAs CodonFreq = 2 * 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table clock = 0 * 0: no clock, unrooted tree, 1: clock, rooted tree model = 0 * models for codons: * 0:one, 1:b, 2:2 or more dn/ds ratios for branches NSsites = 0 icode = 0 * dn/ds among sites. 0:no variation, 1:neutral, 2:positive * 0:standard genetic code; 1:mammalian mt; 2-10:see below fix_kappa = 0 * 1: kappa fixed, 0: kappa to be estimated kappa = 2 * initial or fixed kappa fix_omega = 0 * 1: omega or omega_1 fixed, 0: estimate omega = 1 * initial or fixed omega, for codons or codon-transltd AAs fix_alpha = 1 * 0: estimate gamma shape parameter; 1: fix it at alpha alpha =.0 * initial or fixed alpha, 0:infinity (constant rate) Malpha = 0 * different alphas for genes ncatg = 4 * # of categories in the dg or AdG models of rates getse = 0 * 0: don't want them, 1: want S.E.s of estimates RateAncestor = 1 * (1/0): rates (alpha>0) or ancestral states (alpha=0) fix_blength = 1 * 0: ignore, -1: random, 1: initial, 2: fixed method = 0 * 0: simultaneous; 1: one branch at a time

54 Models } Branch models - is ω for a given branch (or a group of branches) different from the rest of the tree? } Site models - how many codon classes with respect to ω are there (is the selection similar for all the sites)? } Branch and site models - are there differently selected codon classes in a specific branch?

55 Brach models } Null hypothesis - ω is the same in all the branches } Test hypothesis - there are branches where ω is significantly different from the rest of the tree } The free model - a specific ω value for each branch } df = difference in parameter count (each specified branch adds 1)

56 Branch models seqfile = treefile = outfile = noisy = 9 verbose = 2 runmode = 0 * 0,1,2,3,9: how much rubbish on the screen * 1: detailed output, 0: concise output * 0: user tree; 1: semi-automatic; 2: automatic * 3: StepwiseAddition; (4,5):PerturbationNNI seqtype = 1 * 1:codons; 2:AAs; 3:codons-->AAs CodonFreq = 2 * 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table clock = 0 * 0: no clock, unrooted tree, 1: clock, rooted tree model = 0 * models for codons: * 0:one, 1:b, 2:2 or more dn/ds ratios for branches NSsites = 0 icode = 0 * dn/ds among sites. 0:no variation, 1:neutral, 2:positive * 0:standard genetic code; 1:mammalian mt; 2-10:see below fix_kappa = 0 * 1: kappa fixed, 0: kappa to be estimated kappa = 2 * initial or fixed kappa fix_omega = 0 * 1: omega or omega_1 fixed, 0: estimate omega = 1 * initial or fixed omega, for codons or codon-transltd AAs fix_alpha = 1 * 0: estimate gamma shape parameter; 1: fix it at alpha alpha =.0 * initial or fixed alpha, 0:infinity (constant rate) Malpha = 0 * different alphas for genes ncatg = 4 * # of categories in the dg or AdG models of rates getse = 0 * 0: don't want them, 1: want S.E.s of estimates RateAncestor = 1 * (1/0): rates (alpha>0) or ancestral states (alpha=0) fix_blength = 1 * 0: ignore, -1: random, 1: initial, 2: fixed method = 0 * 0: simultaneous; 1: one branch at a time

57 Site models } Assume constant ω in all branches } Null hypothesis (M1a): two classes of codons: } } neutral (ω=1) under purifying selection (ω<1) } Test hypothesis (M2a): three classes of codons: } } } neutral (ω=1) under purifying selection (ω<1) under positive selection (ω>1) } Also other, more complex models (see PAML documentation)

58 Site models seqfile = treefile = outfile = noisy = 9 verbose = 2 runmode = 0 * 0,1,2,3,9: how much rubbish on the screen * 1: detailed output, 0: concise output * 0: user tree; 1: semi-automatic; 2: automatic * 3: StepwiseAddition; (4,5):PerturbationNNI seqtype = 1 * 1:codons; 2:AAs; 3:codons-->AAs CodonFreq = 2 * 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table clock = 0 * 0: no clock, unrooted tree, 1: clock, rooted tree model = 0 * models for codons: * 0:one, 1:b, 2:2 or more dn/ds ratios for branches NSsites = 0 icode = 0 * dn/ds among sites. 0:no variation, 1:neutral, 2:positive * 0:standard genetic code; 1:mammalian mt; 2-10:see below fix_kappa = 0 * 1: kappa fixed, 0: kappa to be estimated kappa = 2 * initial or fixed kappa fix_omega = 0 * 1: omega or omega_1 fixed, 0: estimate omega = 1 * initial or fixed omega, for codons or codon-transltd AAs fix_alpha = 1 * 0: estimate gamma shape parameter; 1: fix it at alpha alpha =.0 * initial or fixed alpha, 0:infinity (constant rate) Malpha = 0 * different alphas for genes ncatg = 4 * # of categories in the dg or AdG models of rates getse = 0 * 0: don't want them, 1: want S.E.s of estimates RateAncestor = 1 * (1/0): rates (alpha>0) or ancestral states (alpha=0) fix_blength = 1 * 0: ignore, -1: random, 1: initial, 2: fixed method = 0 * 0: simultaneous; 1: one branch at a time

59 Branch and site models } In specified branches an additional class of codons with ω>1 } Three classes of codons: } } } neutral (ω=1) under purifying selection (ω<1) under positive selection (ω>1) } Null hypothesis: in the third class ω=1 (no positive selection)

60 Branch and site models seqfile = treefile = outfile = noisy = 9 verbose = 2 runmode = 0 * 0,1,2,3,9: how much rubbish on the screen * 1: detailed output, 0: concise output * 0: user tree; 1: semi-automatic; 2: automatic * 3: StepwiseAddition; (4,5):PerturbationNNI seqtype = 1 * 1:codons; 2:AAs; 3:codons-->AAs CodonFreq = 2 * 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table clock = 0 * 0: no clock, unrooted tree, 1: clock, rooted tree model = 2 * models for codons: * 0:one, 1:b, 2:2 or more dn/ds ratios for branches NSsites = 2 icode = 0 * dn/ds among sites. 0:no variation, 1:neutral, 2:positive * 0:standard genetic code; 1:mammalian mt; 2-10:see below fix_kappa = 0 * 1: kappa fixed, 0: kappa to be estimated kappa = 2 * initial or fixed kappa fix_omega = 0 * 1: omega or omega_1 fixed, 0: estimate omega = 1 * initial or fixed omega, for codons or codon-transltd AAs fix_alpha = 1 * 0: estimate gamma shape parameter; 1: fix it at alpha alpha =.0 * initial or fixed alpha, 0:infinity (constant rate) Malpha = 0 * different alphas for genes ncatg = 4 * # of categories in the dg or AdG models of rates getse = 0 * 0: don't want them, 1: want S.E.s of estimates RateAncestor = 1 * (1/0): rates (alpha>0) or ancestral states (alpha=0) fix_blength = 1 * 0: ignore, -1: random, 1: initial, 2: fixed method = 0 * 0: simultaneous; 1: one branch at a time