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.

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The dynamics of positive selection on the mammalian tree

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


Positive selection in six mammalian genomes

human

chimp

macaque

mouse

rat

dog

0.05

subst/site

Positive selection in six mammalian genomes

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.


Codon models

0 i,j differ by > 1 nucleotide

ji, j synonymous transversion

j  i, j synonymous transition

j  i, j nonsynonymous transversion

j   i, j nonsynonymous transition

Qij=

< 1purifying selection

  • =1neutral evolution

  • > 1positive selection

where

 : transition/transversion rate ratio

j : equilibrium frequency of codon j

 : nonsynonymous/synonymous rate ratio

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


Branch site likelihood ratio tests lrts
Branch-Site LikelihoodRatio Tests (LRTs)

  • 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)


400

human

chimp

hominid

macaque

10

18

7

10

rodent

branch

rodent

clade

primate

branch

primate

clade

56

61

21

24

Branch and clade LRTs

Total: 544 positively selected genes (PSGs) identified



2 9 1 511 possible selection histories on the 9 branch mammalian phylogeny

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


Why baysian model selection
Why Baysian Model Selection?

  • 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.


Bayesian switching model
Bayesian Switching Model

  • 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 model1
Bayesian Switching Model

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

Z=(Z1,…,ZN) be the set of selection histories, with Zidenoting 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,


Mapping selection histories to switches cont

(1,1)

(1,1)

(1,1)

(1,1)

(0,0)

(0,1)

(1,1)

Mapping selection histories to switches (cont.)

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



Putting everything together
Putting everything together …

with

(Beta distrib =1, =9)

(Likelihoods from codon models

assuming selection histories Zj)

(Product relevant switching prob)


Gibbs sampling
Gibbs sampling

Variables Z and  are unobserved. We sample from the

joint posterior distribution

by a Gibbs sampler that alternates between sampling

each Zi conditional on Xi and previously sampled  and

sampling  conditional on a previously sampled Z.



Episodic selection on the mammalian tree
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.


Inferred number of genes under positive selection
Inferred Number of Genes Under Positive Selection

(119-162)

(183-232)

(32-62)

(234 -327)

(219-257)

(338-382)

(318-360)

(255-325)

(357-426)

(204-278)

(213-292)

(281-333)


Complement components c7 and c8b
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)


Glycoprotein hormones gga
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


Summary and future work
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?


Resource
Resource

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


Thanks

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 Nielsen

Rute Da Fonseca

NIH and NSF for funding


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