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1 The Dynamics of Positive Selection on the Mammalian Tree Carolin Kosiol Cornell University <[email protected] > int with: Tomas Vinar, Rute Da Fonseca, Melissa Hub rlos Bustamante, Rasmus Nielsen and Adam Siepel

The Dynamics of Positive Selection on the Mammalian Tree

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The Dynamics of Positive Selection on the Mammalian Tree. Carolin Kosiol Cornell University < [email protected] >. Joint with: Tomas Vinar, Rute Da Fonseca, Melissa Hubisz, Carlos Bustamante, Rasmus Nielsen and Adam Siepel. human. chimp. macaque. mouse. rat. dog. 0.05 subst/site. - PowerPoint PPT Presentation

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Page 1: The Dynamics of Positive Selection on the Mammalian Tree

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The Dynamics of Positive Selection on the Mammalian Tree

Carolin KosiolCornell University

<[email protected]>

Joint with: Tomas Vinar, Rute Da Fonseca, Melissa Hubisz,Carlos Bustamante, Rasmus Nielsen and Adam Siepel

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6 high-quality genomes of eutherian mammals

16529 human / chimp / macaque / mouse / rat / dog orthologous genes.

544 genes identified to be under positive selection using codon models.

Positive selection in six mammalian genomes

0.05

subst/site

human

macaque

mouse

rat

dog

chimp

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Qij

=

0 i, j differ by > 1 nucleotide

j i, j synonymous transversion

j i, j synonymous transition

j i, j nonsynonymous transversion

j i, j nonsynonymous transition

(Goldman &Yang 1994,Yang et al. , 2000)

where : transition/transversion rate ratioj : equilibrium frequency of codon j : nonsynonymous/synonymous rate ratio

Codon models

< 1 purifying selection = 1 neutral evolution > 1 positive selection

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• Based on continuous-time Markov models of codon evolution

• Compare null model allowing for negative

selection (ω<1) or neutral evolution (ω=1)

with alternative model additionally allowing

for positive selection (ω>1)

• Both models allow ω to vary across sites

• Can have foreground branches with PS and background branches without

• Applied separately to each gene

(Nielsen & Yang, 1998; Yang & Nielsen, 2002)

Branch-Site LikelihoodRatio Tests (LRTs)

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5Total: 544 positively selected genes (PSGs) identified

10 18 7 10

chimp macaquehuman hominid

400

Branch and clade LRTs

6156 21 24

primateclade

primatebranch

rodentclade

rodentbranch

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Co-evolution in complement immunity

P<0.05

FDR<0.05

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29-1 = 511 possible selection histories on the 9 branch

mammalian phylogeny

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• Many of the likelihoods of the 511 models might be very similar or identical.

• Models are not nested.

• Bayesian analysis looks at distribution of selection histories.

• Bayesian analysis allows “soft” (probabilistic) choices of selection histories.

• We can compute prevalence of selection on individual branches and clades that considers uncertainty of selection histories.

Why Baysian Model Selection?

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Two evolutionary modes:

Selected

Non-selected

Parameters describing the switching process:

b,G : probability that gene gains positive selection on

branch b

b,L : probability that gene loses positive selection on

branch b

Bayesian Switching Model

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X =(X1, …XN) be the alignment data, with Xi alignment of ith gene

Z=(Z1,…,ZN) be the set of selection histories, with Zi denoting history of ith gene.

is set of switching parameters

Assume independence of genes X and histories Z, and conditional independence X and given Z. Thus,

Bayesian Switching Model

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Mapping selection histories to switches (cont.)

(0,0) (0,1)

(1,1) (1,1)(1,1)

(1,1)

(1,1)

Gain of pos. selection (0,1) : nbG

Absence of gain of pos. selection (0,0) : 1- nbG

Loss of pos. selection (0,1) : nbL

Absence of loss pos. selection (1,1) : 1- nbL

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Bayesian Switching model

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Putting everything together …

with

(Beta distrib =1, =9)

(Product relevant switching prob)

(Likelihoods from codon models

assuming selection histories Zj)

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Gibbs sampling

Variables Z and are unobserved. We sample from the joint posterior distribution

by a Gibbs sampler that alternates between samplingeach Zi conditional on Xi and previously sampled and sampling conditional on a previously sampled Z.

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Inferred Rates of Gain and Loss

gain loss

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Episodic selection on the mammalian tree

• Most genes appear to have switched between evolutionary modes multiple times.

• Posterior expected number of modes switches 1.6 (0.6 gains, 1.0 loses)

• An expected 95% of PSGs have experienced at least once, 53% at least twice.

• These observations are qualitatively in agreement with Gillespie’s episodic molecular clock.

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Inferred Number of Genes Under Positive Selection

(281-333)

(213-292)

(255-325)

(204-278)(357-426)

(338-382)

(32-62) (234 -327)

(219-257)

(183-232)(119-162)

(318-360)

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Complement components C7 and C8B

• Components C7 and C8B encode proteases in the membrane attack complex

• Differences in complement proteases are thought to explain certain differences in immune responses of humans and rodents.

C7: PP=0.98 C8B: PP=0.93

(Puente et al, 2003)

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Glycoprotein hormones GGA

• CGA is alpha subunit of

chorionic gonadotropin,

luteinizing hormone,

follicle stimulating, and thyroid

stimulating hormone.

• The alpha subunits of 4 hormones are identical, however,

their beta chains are unique and confer biological specificity.

• Beta subunits CGB1 and CGB2 are thought to have

originated from gene duplication in the common ancestor of

humans and great apes.

PP = 0.82

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Summary and Future Work

• Bayesian analysis allows the study of patterns and the episodic nature of positive selection on the mammalian tree.

• Most probable selection histories can be identified for individual genes.

• Ideally, we like to model mode switches in continuous time.

• Compare functions of genes with high and low expected number of switches.

• Is the selection history predictive of function?

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Resource

http://compgen.bscb.cornell.edu/projects/mammal-psg/

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Thanks

Siepel Lab (Cornell)Adam Siepel, Tomas Vinar, Brona Brejova,

Adam Diehl, Andre Luis Martins

Bustamante Lab (Cornell)Carlos Bustamante, Adam Boyko, Adam Auton, Keyan Zhao,

Abra Brisbin, Kasia Bryc, Jeremiah Degenhardt,

Lin Li, Kirk Lohmueller, Weisha Michelle Zhu, Amit Indap

Nielsen lab (Berkeley)Rasmus NielsenRute Da Fonseca

NIH and NSF for funding