species trees from genomescale data Tandy Warnow The University of Illinois Orangutan Gorilla Chimpanzee Human From the Tree of the Life Website University of Arizona Phylogeny evolutionary tree ID: 830284
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Slide1
New methods for estimating species trees from genome-scale data
Tandy WarnowThe University of Illinois
Slide2Orangutan
Gorilla
Chimpanzee
Human
From the Tree of the Life Website,
University of Arizona
Phylogeny
(evolutionary tree)
Slide3Orangutan
Gorilla
Chimpanzee
Human
From the Tree of the Life Website,
University of Arizona
Sampling multiple genes from multiple species
Slide4Slide5Incomplete Lineage Sorting (ILS) is a dominant cause of
gene tree heterogeneity
Slide6This talkGene tree heterogeneity due to incomplete lineage sorting, modelled by the multi-species coalescent (MSC)
Statistically consistent estimation of species trees under the MSC, and the impact
of gene tree estimation error“Statistical binning” (Science 2014) – improving gene tree estimation, and hence species tree estimation
Open questions
Slide7Gene trees inside the species tree (Coalescent Process)
Present
Past
Courtesy James
Degnan
Gorilla and Orangutan are not siblings in the species tree, but they are in the gene tree.
Slide8Incomplete Lineage Sorting (ILS)Confounds phylogenetic analysis for many groups: Hominids, Birds, Yeast, Animals, Toads, Fish, Fungi, etc.
There is substantial debate about how to analyze phylogenomic datasets in the presence of ILS.
Slide9. . .
Analyze
separately
Summary Method
Two competing approaches
gene 1
gene 2
. . .
gene
k
. . .
Concatenation
Species
Slide10Statistical Consistencyerror
Data
Slide11Slide12. . .
What about summary methods?
Slide13. . .
What about summary methods?
Techniques:
Most frequent gene tree?
Consensus of gene trees?
Other?
Slide14Statistically consistent under ILS?
Coalescent-based summary methods:MP
-EST (Liu et al. 2010): maximum pseudo-likelihood estimation of rooted species
tree based on rooted triplet tree distribution – YESBUCKy-pop (
Ané and Larget 2010): quartet-based Bayesian species tree estimation –
YESAnd many others (ASTRAL, ASTRID, NJst, GLASS, etc.)
Co-estimation methods: *BEAST (Heled
and Drummond 2009): Bayesian co-estimation of gene trees and species trees
– YESSingle-site methods (SVDquartets, METAL, SNAPP, and others)
Slide15Statistically consistent under ILS?
Coalescent-based summary methods:MP
-EST (Liu et al. 2010): maximum pseudo-likelihood estimation of rooted species
tree based on rooted triplet tree distribution – YESBUCKy-pop (
Ané and Larget 2010): quartet-based Bayesian species tree estimation –
YESAnd many others (ASTRAL, ASTRID, NJst, GLASS, etc.)
Co-estimation methods: *BEAST (Heled
and Drummond 2009): Bayesian co-estimation of gene trees and species trees
– YESSingle-site methods (SVDquartets, METAL, SNAPP, and others) - YES
CA-ML (Concatenation
using unpartitioned
maximum likelihood) - NO
MDC – NO
GC (Greedy Consensus) – NO
MRP (supertree method) –
NO
Slide16Statistically consistent under ILS?
Coalescent-based summary methods:MP
-EST (Liu et al. 2010): maximum pseudo-likelihood estimation of rooted species
tree based on rooted triplet tree distribution – YESBUCKy-pop (
Ané and Larget 2010): quartet-based Bayesian species tree estimation –
YESAnd many others (ASTRAL, ASTRID, NJst, GLASS, etc.)
Co-estimation methods: *BEAST (Heled
and Drummond 2009): Bayesian co-estimation of gene trees and species trees
– YESSingle-site methods (SVDquartets, METAL, SNAPP, and others) - YESCA-ML (Concatenation using unpartitioned
maximum likelihood) -
NO
MDC –
NO
GC (Greedy Consensus) –
NO
MRP (supertree method) –
NO
Slide17Results on 11-taxon datasets with weak ILS
*
BEAST more accurate than summary methods (MP-EST, BUCKy
, etc) CA-ML (concatenated analysis) most accurate Datasets from Chung and
Ané, 2011 Bayzid
& Warnow, Bioinformatics 2013
Slide18Problem: poor gene trees
Summary methods combine estimated gene trees, not true gene trees.
The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic signal, and result in poorly estimated gene trees.
Species trees obtained by combining poorly estimated gene trees have poor accuracy.
Slide19Problem: poor gene trees
Summary methods combine estimated gene trees, not true gene trees.
The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic signal, and result in
poorly estimated gene trees.Species trees obtained by combining poorly estimated
gene trees have poor accuracy.
Slide20Problem: poor gene trees
Summary methods combine estimated gene trees, not true gene trees.
The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic signal, and result in poorly estimated gene trees.
Species trees obtained by combining poorly estimated gene trees have poor accuracy.
Slide21Summary methods combine estimated gene trees, not true gene trees.
The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic
signal, and result in poorly estimated gene trees.Species trees obtained by combining poorly estimated
gene trees have poor accuracy.
TYPICAL PHYLOGENOMICS PROBLEM: many poor gene trees
Slide22Summary methods combine estimated gene trees, not true gene trees.
The individual gene sequence alignments in the 11-taxon datasets have poor phylogenetic
signal, and result in poorly estimated gene trees.Species trees obtained by combining poorly estimated
gene trees have poor accuracy.
THIS IS A KEY ISSUE IN THE DEBATE ABOUT HOW TO COMPUTE SPECIES TREES
Slide23Statistical Consistency for summary methods
error
Data
Data are gene trees, presumed to be randomly sampled
true gene trees.
Slide24Avian Phylogenomics Project
E
Jarvis,
HHMI
G
Zhang,
BGI
Approx. 50 species, whole
genomes, 14,000 loci
Published Science 2014
MTP Gilbert,
Copenhagen
S.
Mirarab
Md
. S.
Bayzid
, UT-
Austin UT-Austin
T. WarnowUT-Austin
Plus many many other people…
Challenges:Massive gene tree conflict suggestive of ILSCoalescent-based analysis using MP-EST produced tree that conflicted with concatenation analysisMost gene trees had very low bootstrap support, suggestive of gene tree estimation
error
Slide25Avian Phylogenomics Project
E
Jarvis,
HHMI
G
Zhang,
BGI
Approx. 50 species, whole
genomes, 14,000 loci
MTP Gilbert,
Copenhagen
S.
Mirarab
Md
. S.
Bayzid
, UT-
Austin UT-Austin
T. Warnow
UT-Austin
Plus many many other people…Solution:
Statistical BinningImproves coalescent-based species tree estimation by improving gene trees (Mirarab, Bayzid, Boussau, and Warnow, Science 2014
)Avian species tree estimated using
Statistical Binning with MP-EST
(Jarvis, Mirarab, et al.,
Science
2014)
Slide26Gene Tree Estimation Error can be due to insufficient data
error
Data
Data are sites in an alignment for a c-gene
Slide27Unweighted statistical binning (Science 2014)
Given multiple sequence alignments for a set of loci:
Estimate ML gene trees with bootstrap support Bin genes based on gene tree compatibility after collapsing low support branches, producing “supergene alignments”
Compute “supergene trees” (one for each bin), using fully partitioned maximum likelihoodApply coalescent-based summary method to the supergene trees, requiring that the summary method be statistically consistent under the MSC
Slide28Unweighted statistical binning pipelines are not statistically consistent under GTR+MSC
Easy proof: As the number of sites per locus increaseA
ll estimated gene trees converge to the true gene tree and have bootstrap support that converges to 1 (Steel 2014)For every bin, with probability converging to 1, the genes in the bin have the same tree topology.Fully partitioned GTR ML analysis of each bin converges to a tree with the common topology of the genes in the bin.
As the number of loci increase, every gene tree topology appears with probability converging to 1.Cannot infer the species tree from the flat distribution of gene trees!
Slide29Weighted statistical binning (PLOS One 2015)
Given multiple sequence alignments for a set of loci:
Estimate ML gene trees with bootstrap support Bin genes based on gene tree compatibility after collapsing low support branches, producing “supergene alignments”
Compute “supergene trees” (one for each bin), using fully partitioned maximum likelihood
Replace original gene tree by the new supergene tree (equivalently, replicate supergene trees by the size of each bin)Apply coalescent-based summary method to the supergene trees, requiring that the summary method be statistically consistent under the MSC
Slide30WSB pipelines are statistically consistent under GTR+MSCEasy proof:
As the number of sites per locus increaseAll estimated gene trees converge to the true gene tree and have bootstrap support that converges to 1 (Steel 2014)
For every bin, with probability converging to 1, the genes in the bin have the same tree topology Fully partitioned GTR ML analysis of each bin converges to a tree with the common topology of the genes in the bin
Hence as the number of sites per locus and number of loci both increase, WSB followed by a statistically consistent summary method will converge in probability to the true species tree. Q.E.D.
Slide31Slide32Statistical binning vs. unbinned
Mirarab, et al., Science 2014 (Unweighted statistical binning)
Binning produces bins with approximate 5 to 7 genes each
Datasets: 11-taxon strongILS datasets with 50 genes, Chung and Ané, Systematic Biology
Slide33Comparing Binned and Un-binned MP-EST on the Avian Dataset
Unbinned
MP-ESTs
trongly rejects Columbea, a majorfinding by Jarvis, Mirarab,et
al.Binned MP-EST islargely consistent with the MLconcatenation
analysis.The trees presentedin Science 2014 werethe ML concatenationa
nd Binned MP-EST
Slide34Summary
Unpartitioned concatenation using maximum likelihood is statistically inconsistent under the MSC (Roch and Steel 2014, see discussion in Warnow PLOS Currents 2015)
Gene tree estimation error impacts species tree estimation (multiple papers)Statistical binning (Mirarab et al. Science 2014) improves coalescent-based species tree estimation from multiple genes, used in Avian Tree (Jarvis, Mirarab, et al. Science 2014).Weighted statistical binning pipelines are statistically consistent under GTR+MSC, but unweighted statistical binning pipelines are not (Bayzid et al., PLOS One 2015)
Slide35Bounded number of sites per locus?Do any summary methods converge to the species tree as the number of loci increase, but where each locus has only a constant number of sites?
Roch & Warnow, Systematic Biology 2015
:Yes under the strong molecular clock (even for a single site per locus)Very
limited results otherwise
Slide36Open QuestionsIs fully partitioned ML statistically consistent or inconsistent under the MSC? (Note: proof by
Roch and Steel for unpartitioned ML will not easily extend to fully partitioned)
Are any of the standard summary methods statistically consistent for bounded number of sites per locus, but unbounded number of loci?Are the co-estimation methods (e.g., *BEAST and BEST) statistically consistent for bounded number of sites per locus but unbounded number of loci?
Slide37Open QuestionsWhy does concatenation using ML (whether unpartitioned or partitioned) produce such good accuracy under many conditions?
Why does statistical binning improve accuracy under many conditions?
Slide38Acknowledgments
PhD students: Siavash Mirarab* (now Assistant Professor at UCSD ECE) and Md. S. Bayzid**
Bastien
Boussau
(CNRS, Lyon)
Sébastien
Roch
(Wisconsin)
Funding
: Guggenheim Foundation, Packard, NSF, Microsoft Research New England, David
Bruton
Jr. Centennial Professorship, TACC (Texas Advanced Computing Center), and GEBI.
TACC
and UTCS
c
omputational resources
* Supported by HHMI Predoctoral Fellowship
** Supported by Fulbright Foundation Predoctoral Fellowship