Article

Heterogeneous Models Place the Root of the Placental MammalPhylogenyClaire C Morgan12 Peter G Foster3 Andrew E Webb12 Davide Pisani45 James O McInerney4 andMary J OrsquoConnellz12

1Bioinformatics and Molecular Evolution Group School of Biotechnology Dublin City University Glasnevin Dublin Ireland2Centre for ScientiEc Computing amp Complex Systems Modeling (SCI-SYM) Dublin City University Glasnevin Dublin Ireland3Department of Life Sciences Natural History Museum London United Kingdom4Molecular Evolution and Bioinformatics Unit Department of Biology National University of Ireland Maynooth Co Kildare Ireland5School of Biological Sciences and School of Earth Sciences University of Bristol Bristol United KingdomzPresent address Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology Harvard University

Cambridge MA

Corresponding author E-mail maryoconnelldcuie

Associate editor Emma Teeling

Abstract

Heterogeneity among life traits in mammals has resulted in considerable phylogenetic conflict particularly concerningthe position of the placental root Layered upon this are gene- and lineage-specific variation in amino acid substitutionrates and compositional biases Life trait variations that may impact upon mutational rates are longevity metabolic ratebody size and germ line generation time Over the past 12 years three main conflicting hypotheses have emerged for theplacement of the placental root These hypotheses place the Atlantogenata (common ancestor of Xenarthra plusAfrotheria) the Afrotheria or the Xenarthra as the sister group to all other placental mammals Model adequacy iscritical for accurate tree reconstruction and by failing to account for these compositional and character exchangeheterogeneities across the tree and data set previous studies have not provided a strongly supported hypothesis forthe placental root For the first time models that accommodate both tree and data set heterogeneity have been appliedto mammal data Here we show the impact of accurate model assignment and the importance of data sets in accom-modating model parameters while maintaining the power to reject competing hypotheses Through these sophisticatedmethods we demonstrate the importance of model adequacy data set power and provide strong support for theAtlantogenata over other competing hypotheses for the position of the placental root

Key words mammal phylogeny phylogenetic reconstruction evolutionary models placental root heterogeneousmodeling

IntroductionThere are conflicting views over the root of the placentalmammal phylogeny (Meredith et al 2011 McCormacket al 2012 Song et al 2012 OrsquoLeary et al 2013) The conflictis largely due to the impact of compressed cladogenesis aphenomenon shown to have a major negative impact on theresolution of the metazoan phylogeny for example (Rokaset al 2005) In the case of the diversification of placentalmammals the compressed cladogenesis is so severe thatthe phylogenetic signature has proven very difficult to deci-pher Following a large number of studies using different datasets data types and approaches we now have a smallnumber of areas of conflict remaining in the mammal treethey concern 1) the position of the root of the placentalSuperorders (Murphy et al 2001a Meredith et al 2011McCormack et al 2012 Song et al 2012 OrsquoLeary et al2013) 2) the branching of extant placental orders(Stanhope et al 1998 Waddell et al 1999) and 3) the pre-cise placement of many individual placental species

(Janecka et al 2007) The three major competing hypothesesfor the position of the root of the placental phylogeny areillustrated in figure 1 In summary these alternative hypoth-eses place the following as the sister group of all remainingplacentals 1) the Afrotheria for example elephants and man-atees (Murphy et al 2001b McCormack et al 2012) 2) theXenarthra for example armadillos and sloths (OrsquoLeary et al2013) and 3) the common ancestor of Afrotheria andXenarthra (Atlantogenata hypothesis) (Meredith et al 2011Song et al 2012) Some of the most recent contributions tothe field of mammal phylogeny have used ultraconservedelements (McCormack et al 2012) and morphological data(specifically more than 4500 morphological measurementsacross 40 extinct and 86 extant placental mammals) (OrsquoLearyet al 2013) resolving the Afrotheria and Xenarthra respec-tively Interestingly the work by OrsquoLeary et al (2013) con-cluded among other things that the Afrotheria did notoriginate in Africa but in the Americas Consilience acrossthese different data types would be most desirable but toachieve this it would be necessary to have a full

The Author 2013 Published by Oxford University Press on behalf of the Society for Molecular Biology and EvolutionThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (httpcreativecommonsorglicensesby-nc30) which permits non-commercial re-use distribution and reproduction in any mediumprovided the original work is properly cited For commercial re-use please contact journalspermissionsoupcom Open AccessMol Biol Evol 30(9)2145ndash2156 doi101093molbevmst117 Advance Access publication June 29 2013 2145

understanding of the evolutionary dynamics of all of thesedata to adequately model them

Retroposons are attractive alternative data type that is lesslikely to be subject to model misspecification However aprevious analysis of these rare genomic events by Nishiharaet al (2009) showed similar levels of support for all threecompeting hypotheses for the position of the placental root(Nishihara et al 2009) There is some evidence that retro-posons are not entirely devoid of homoplasy (Han et al2011) which may contribute to signal conflict Here weshow how adequate modeling can be achieved for protein-coding molecular data

The current debate over the position of the placental rootand indeed the branching patterns within certain Superorders(eg Laurasiatheria) is due in no small part to incorrectortholog identification (Philippe et al 2011) long branch at-traction (LBA) (Heath et al 2008) saturation of signal(Moreira and Philippe 2000) compositional bias (Foster andHickey 1999) evolutionary rate variation between lineagesand between different characters in a data set (Li et al1987) and model misspecification (Philippe et al 2011)Each of these factors (alone or in combination) can have amajor effect on the resulting topology as outlined later

Comparisons of protein-coding genes in mammalian ge-nomes have shown that there are substantial variations inboth mutational rate and nucleotide composition (Li et al1987 Romiguier et al 2010) The observed heterogeneity canbe influenced by many factors including diet (Yang 1998)disease (Usanga and Luzzatto 1985) and intense sexual selec-tion (Dorus et al 2004) Variations in body size longevity

metabolic rate and germ-line generation time across specieshave also been shown to affect the rate of mutation (Petoet al 1975 Martin and Palumbi 1993 Bleiweiss 1998 Leroiet al 2003 de Magalhaes and Costa 2009 Caulin and Maley2011) Smaller shorter-lived mammals with high metabolicrates and short germ-line generation times accumulate mu-tations at a higher rate (Li et al 1996) With such variation inmutational rate across lineages these data are also likely tosuffer the effects of LBA Holton and Pisani (2010) have shownthat LBA has led to the incorrect resolution of Coelomata as aclade the influence of this systematic bias is also a concern formammal phylogeny reconstruction GC-biased gene conver-sion in the DNA repair machinery (Galtier 2003) results in ahigher GC content over time for those species with highermutational rates and this has resulted in mammal genomedata manifesting compositional biases across species(Romiguier et al 2010) By disregarding compositional het-erogeneity in a data set (ie by not modeling it adequately)we can consistently identify the wrong tree as optimal (Foster2004) Foster gave examples where homogeneous modelsgave an incorrect topology but using a tree-heterogeneouscomposition model obtained the correct topology and wasalso shown to fit the data adequately (Foster 2004 Fosteret al 2009) The importance of modeling both compositionalheterogeneity across the tree and heterogeneity in exchangerates over the data set has been shown by Cox et al (2008)where strong support for the incorrect topology was obtainedif one employed models that did not account for these typesof heterogeneity in the data

In this article we employ two major classes of heteroge-neous modeling first those that accommodate compositionalheterogeneity and exchange rate heterogeneity across thephylogeny As previously described by Foster et al (2009)these are node-discrete composition heterogeneous(NDCH) and node-discrete rate matrix heterogeneous(NDRH) models The second class of heterogeneous modelsapplied are those that allow for heterogeneity across the dataset (Lartillot and Philippe 2004) All previous attempts toreconstruct the mammal phylogeny using molecular datahave used homogeneous models that do not account forvariation in composition or exchange rates across the treeor the data set (Murphy et al 2001b Bininda-Emonds et al2007 Meredith et al 2011) In contrast here we will usemodels that adequately describe the complex reality of theevolutionary processes However more sophisticated modelsdemand more data to discriminate among competing hy-potheses For example alignment lengths of greater than1000 positions (ie concatenated data sets) have beenshown to be necessary for profile mixture models to reliablycalculate the shapes of the profiles of the data (Quang et al2008) Therefore if heterogeneous models are to be employedthen the size and the quality of the data set are critical

To date there have been many studies that used conca-tenated data sets to investigate the position of the placentalroot (Murphy et al 2001b 2007 Kriegs et al 2006 Asher 2007Hallstrom and Janke 2010) These data sets have includednuclear and mitochondrial genes (with protein-codinggenes treated either as nucleotide or amino acid sequences)

FIG 1 Three major rooting hypotheses for placental mammals Thethree major competing hypotheses for the position of the root aredepicted (A) The Afrotheria root (B) the Xenarthra root and (C) theAtlantogenata root

2146

Morgan et al doi101093molbevmst117 MBE

noncoding DNA morphological data and rare genomicevents (Murphy et al 2001b 2007 Kriegs et al 2006Hallstrom and Janke 2010) It is now clear however thatnot all data sets are equally suitable for all phylogenetic ques-tions at all phylogenetic depths as shown most recently bySong et al (2012) The work of Brown et al (1982) showedthat mitochondrial genes and noncoding DNA sequencesaccumulate mutations at a faster rate than nuclear protein-coding genes making them less appropriate for resolvingdeep nodes on a phylogeny In addition DNA sequencesare more prone to mutational saturation than amino aciddata sets (Kosiol et al 2007) and might be more stronglyaffected by biases related to lineage-specific codon usage pref-erences (Rota-Stabelli et al 2013)

To fully address the root of the placental mammals evo-lutionary models that account for heterogeneity in both com-position and the rate of mutational change (ie exchangerate) across the tree and the data must be employed(Foster et al 2009) Along with modeling these features it isessential for the data set to have the power to reject alterna-tive hypotheses and to be sufficiently large to meet the re-quirements of the increase in parameterization Here we haveexploited recent advances in modeling heterogeneity of com-position and exchange rates across the tree and the data in aBayesian framework and using a data set generated fromcompleted genomes we address the problem of the root ofthe placental mammals

Results and DiscussionFirst we set out to determine whether there was a significantimprovement in fit with the use of heterogeneous modelsover homogeneous models for mammal data sets To per-form this test we used two sources of data 1) a previouslypublished data set containing 66 taxa and a maximum align-ment length of 9789 base pairs referred to as ldquo66TaxonSetrdquo(see Materials and Methods) (4) and 2) a data set assembledspecifically for this analysis containing 39 taxa and an align-ment length of 23900 amino acids (aa) referred to through-out as ldquo39TaxonSetrdquo We tested the suitability of these datasets for resolving the root of the mammal phylogeny Thetreatment of the data sets and their naming system are de-scribed in table 1

Heterogeneous Models Fit ldquo66TaxonSetrdquo Better thanHomogeneous Models

In the first instance we wished to determine whether thehomogeneous models were adequate for 66TaxonSet To de-termine which model describes 66TaxonSet we analyzed thedata at three levels in total the first two were the nucleotidelevel (66TaxonSet_nuc) and the amino acid level(66TaxonSet_aa) Recoding into Dayhoff categories tends toameliorate compositional heterogeneity and affords us theopportunity to apply GTR-like rate matrices to these resizeddata sets using a relatively small number of free parameters(Hrdy et al 2004) Therefore NDRH models were applied tothe Dayhoff recoded data set (66TaxonSet_day) Thesedata were recoded into a total of 6 Dayhoff categories as

follows Category 1 [C] Category 2 [S T P A and G]Category 3 [N D E and Q] Category 4 [H R and K]Category 5 [M I L and V] and Category 6 [F Y and W](Hrdy et al 2004) The 66TaxonSet_nuc and 66TaxonSet_aashowed compositional heterogeneity where 13 out of 66 hadP values of approximately 0002 and 47 out of 66 taxa had Pvalues of 0000 respectively Therefore neither 66TaxonSet_nuc nor _aa fit the homogenous model (P values werenot corrected for multiple comparisons) whereas the66TaxonSet_day was compositionally homogeneous (supple-mentary table S1 Supplementary Material online) These re-sults support the thesis that the nucleotide and amino aciddata sets from 66TaxonSet require models that accommo-date compositional heterogeneity over the tree and the data

We use two approaches to model heterogeneity one is tomodel heterogeneity across the phylogeny using the NDCHNDRH models (Foster 2004) and the second is to modelheterogeneity across the data using the CAT model(Lartillot and Philippe 2004) The NDRH model allows forcharacter state exchange rates to vary over the topologyand the NDCH model allows for the composition to varyover the topology independently of each other The CATmodel developed by Lartillot and Philippe (2004) modelsheterogeneity over the data using a mixture of site profilesHere we will define homogeneous models as those that donot allow for compositional variation or exchange rate vari-ation across the tree or data set that is they do not incor-porate either NDRHNDCH or CAT models described earlierA homogeneous model with a single exchange rate matrix asingle composition vector a proportion of invariable sitesand four categories of gamma distributed associated rate var-iation is denoted as 1GTR + 1C + I + 4 this shorthand no-tation is used throughout the article

Our strategy was to investigate whether models that ac-count for heterogeneity over the phylogeny and data are abetter fit to mammal data than previously employed homo-geneous models The best-fitting homogeneous model wasidentified using ModelGenerator v85 (Keane et al 2006) andthe models accommodating heterogeneity across the phylog-eny were evaluated by progressively increasing the number ofexchange rate matrices and composition vectors (or both)until there was no further improvement from increased pa-rameterization Bayes factor (BF) comparisons were used todetermine when the addition of parameters no longer signif-icantly improved the model fit to the data The differencebetween the fit of the two models to the data set must have2(ln BF)gt6 to be deemed significant (see table 2 for summary

Table 1 Summary of Data Sets Analyzed

Data Sets Data Set Type Alignment Length

66TaxonSet_nuc Nucleotide 9789 bp

66TaxonSet_aa Amino acid 2190 aa

66TaxonSet_day Dayhoff recoded 6 2190 characters

39TaxonSet_aa Amino acid 23900 aa

39TaxonSet_day Dayhoff 23900

NOTEmdashThe name of each data set as per main text the data type and correspond-ing alignment lengths are shown

2147

Rooting the Placental Mammal Phylogeny doi101093molbevmst117 MBE

of models used in 66TaxonSet and supplementary table S2Supplementary Material online for complete list)

BF analysis indicated that there is strong evidence in favorof the heterogeneous model containing three GTR matricesfive composition vectors an invariable sites category and fourcategories of gamma distributed associated rate variationthat is 3GTR + 5C + I + 4 over the homogeneous model(1GTR + 1C + I + 4) 2(ln BF) = 10971 The largest change inBF scores is seen in the shift between the compositionallyhomogeneous model (1GTR + 1C + I + 4) and the modelthat allows for compositional heterogeneity across the tree(1GTR + 2C + I + 4) with 2(ln BF) = 7277 All BF compari-sons are detailed in supplementary table S3 SupplementaryMaterial online Posterior predictive simulations on66TaxonSet_nuc showed that homogeneous models do notfit the data see figure 2A whereas the best fitting model thataccounts for heterogeneity across the phylogeny(3GTR + 5C + I + 4) does fit the data see figure 2B Thisresult indicates that the model employed in the original pub-lication of the 66TaxonSet was not adequate to describe thedata (Murphy et al 2001a)

To evaluate the improvement of fit on increasing compo-sition vectors over amino acid data sets the number of com-position vectors was increased incrementally over the66TaxonSet_aa It was found that models 1JTT + 5C + I +4 1JTT + 6C + I + 4 and 1JTT + 7C + I + 4 did notreach convergence during the Markov Chain Monte Carlo(MCMC) run this is not surprising given each of thesemodels contain over 100 free parameters BF comparisonsindicated that the compositionally heterogeneous model1JTT + 4C + I + 4 provided best fit to the data and isbetter than the compositionally homogeneous model1JTT + 1C + I + 4 by 2(ln BF) = 3694 However posterior

predictive simulations showed that both homogeneous(1JTT + 1C + I + 4) and the best fit model allowing for com-positional heterogeneity over the phylogeny (1JTT +4C + I + 4) fit the data with tail area probabilities of 079and 097 respectively (supplementary fig S1 SupplementaryMaterial online) In summary the posterior predictive simu-lation results show that the compositionally homogeneousmodel was satisfactory in describing the 66TaxonSet_aa how-ever BF comparisons show that the model that accounts forcompositional heterogeneity across the phylogeny describesthe data better

To evaluate the effect of multiple rate matrices on ouramino acid data set and remove a layer of compositionalheterogeneity the 66TaxonSet_aa was recoded to 6Dayhoff categories (66TaxonSet_day) It was found that in-creasing the number of rate matrices improved the model fitas shown in comparison of model 1GTR + 1C + I + 4 withmodel 2GTR + 1C + I + 4 where 2(ln BF) = 670 This is com-pared with the impact of increasing the composition vectorswhere model 1GTR + 1C + I + 4 compared with model1GTR + 2C + I + 4 2(ln BF) = 164 BF comparisons identi-fied model 5GTR + 2C + I + 4 as the best fitting model andwhen compared with the homogeneous model(1GTR + 1C + I + 4) 2(ln BF) = 1693 Posterior predictivesimulations show that both the homogeneous model1GTR + 1C + I + 4 and the heterogeneous model of best-fit 5GTR + 2C + I + 4 were both adequate in describing thedata

Next we wished to pursue the impact of models thataccommodate heterogeneity across the data set and so weapplied profile mixture model CAT to the data BF compar-isons were not employed to test the fit of the CAT model asthey were not implemented in PhyloBayes v32 (Lartillot et al2009) instead we used 10-fold Bayesian cross validation Thefit of data set heterogeneous models CAT and CAT-GTR werecompared against the fit of homogeneous models 1GTR +1C + I + 4 and 1JTT + I + 4 Results show that for both66TaxonSet_nuc and 66TaxonSet_day a 1GTR + I + 4model fits the data best (supplementary table S7Supplementary Material online) For 66TaxonSet_aa CAT isthe best fitting model The results of cross validation com-bined with the BF analyses show that the model using theGTR rate matrix and accommodating compositional hetero-geneity describe the data better than compositionally homo-geneous models Therefore these heterogeneous modelswere used to derive optimal phylogenies for 66TaxonSet_nuc and 66TaxonSet_day whereas the CAT model wasused to find the optimal tree for 66TaxonSet_aa see sum-mary table 3

Phylogenetic Results of Best Fitting Models on66TaxonSet

In contrast to the previously published topology for66TaxonSet there are significant topological differences re-sulting from the application of models that account for het-erogeneity over the phylogeny and the data However theposterior probability (PP) support values for placement of the

Table 2 Models Applied Using P4

Model Number ofGTR RateMatrices

Numberof Composition

Vectors

Parameters

1GTR + 1C + I + 4 1 1 12

2GTR + 1C + I + 4 2 1 18

3GTR + 1C + I + 4 3 1 24

4GTR + 1C + I + 4 4 1 30

5GTR + 1C + I + 4 5 1 36

6GTR + 1C + I + 4 6 1 42

1GTR + 2C + I + 4 1 2 16

1GTR + 3C + I + 4 1 3 20

1GTR + 4C + I + 4 1 4 24

1GTR + 5C + I + 4 1 5 28

1GTR + 6C + I + 4 1 6 32

1GTR + 7C + I + 4 1 7 36

2GTR + 5C + I + 4 2 5 34

3GTR + 5C + I + 4 3 5 40

4GTR + 5C + I + 4 4 5 46

NOTEmdashThe model codes used in this study the number of GTR rate matricesapplied to the data the number of composition vectors estimated and thenumber of free parameters estimated for each model All parameters were free tovary In all cases the proportion of invariable sites (+I) = 03 and the gamma dis-tributed associated rate variation (+) = 07

2148

Morgan et al doi101093molbevmst117 MBE

placental root using the best fitting models remains low66TaxonSet_nuc and 66TaxonSet_aa support the Atlanto-genata hypothesis with a PP of 046 (3GTR + 5C + I + 4)and 051 (CAT) respectively The 66TaxonSet_day supportsthe Xenarthra rooting with a high PP score of 098(5GTR + 2C + I + 4) However comparison of BF scoresshowed that 66TaxonSet_day is unable to reject the Atlanto-genata hypothesis 2(ln BF) = 196 or the Afrotheria rooting2(ln BF) = 524 All BF comparison scores are given in supple-mentary table S4 Supplementary Material online The resultsindicate that although models that accommodate heteroge-neity across the phylogeny and the data described all treat-ments of 66TaxonSet better (66TaxonSet_ -nuc -aa and -day) this data set does not have sufficient power to support asingle topology and reject all other competing phylogenetichypotheses for the placement of the placental mammal root

Heterogeneous Modeling of the 39TaxonSet

Given the current availability of many complete mammalgenomes we moved forward to assemble a new data setreferred to as the 39TaxonSet The data set consists ofsingle copy orthologous families and contains 71700 aligned

nucleotide positions across 39 taxa Overall this data set con-tains fewer taxa but is four times the length of 66TaxonSet_nuc Single gene orthologs were assembled using a strict bestreciprocal Basic Local Alignment Search Tool (Blast) hit ap-proach (see Materials and Methods) These data were ana-lyzed at the 1) nucleotide level (39TaxonSet_nuc) 2) aminoacid level (39TaxonSet_aa) and 3) these data were recodedinto six Dayhoff categories (39TaxonSet_day) in the same wayas for the 66TaxonSet

The 27 genes comprising the 39TaxonSet had an overallGC content in the range 3036ndash4544 across all 39 taxa Themodel fit test showed that 2627 genes tested in the39TaxonSet_nuc were compositionally heterogeneous Thelevel of heterogeneity varied on a gene-by-gene basis overall1727 of the genes tested had at least 2539 compositionallyheterogeneous taxa (P valueslt 005) The results of the com-position test have been provided in supplementary tableS5(D) Supplementary Material online

A total of 25 different models were applied to the39TaxonSet and these ranged from the simplest modelthat is the homogeneous model with one composition vectorand one exchange rate matrix to the more parameter-rich

FIG 2 Fit of alternative models applied to 66TaxonSet_nuc and posterior predictive simulations for the homogeneous model and heterogeneousmodel of ldquobest-fitrdquo for 66TaxonSet_nuc Models are shown on x axis The specifications of each model that is the number of rate matrices andcomposition vectors are given inside the bar that represents that model The y axis shows the ln L score the higher the bar the worse the fit of themodel Inset (A) shows posterior predictive simulations for the homogeneous model (1GTR + 1C + I + 4) Inset (B) shows posterior predictivesimulation for the best-fit heterogeneous model (3GTR + 5C + I + 4) The black bar graphs in insets (A) and (B) represent the posterior predictivesimulations of these models on 66TaxonSet_nuc and the arrow in each inset represents the 2 position in the simulation for the real data

2149

Rooting the Placental Mammal Phylogeny doi101093molbevmst117 MBE

heterogeneous models including for example Six GTR rateexchange matrices and six compositions vectors (for39TaxonSet_nuc) one JTT rate exchange matrix and sevencomposition vectors (for 39TaxonSet_aa) and finally up tofive GTR exchange rate matrices and four estimated compo-sition vectors (39TaxonSet_day) (see supplementary table S2Supplementary Material online for complete set of modelstested) The 39TaxonSet_nuc failed the 2 tests of composi-tional homogeneity (P value = 000) whereas the model fittest (Foster 2004) showed that 1739 taxa were not modeledadequately by the homogeneous model 1GTR + 1C + 4Amino acid data are known to ameliorate problems withcompositional biases possible codon usage biases and in ad-dition they become saturated for superimposed substitutionsmore slowly (Philippe et al 2011) therefore we constructedthe 39TaxonSet_aa data set Although the 39TaxonSet_aadata set passed the 2 tests of compositional homogeneity(P value = 100) there were 35 out of 39 taxa that failed to fitthe expectation of the homogeneous model 1JTT + 4Finally for the 39TaxonSet_day there were 21 out of 39taxa that failed to fit the homogeneous model GTR +1C + 4 (supplementary table S1 Supplementary Materialonline)

BF analyses were employed to determine whether therewas strong evidence for the application of more parameterrich models over less parameter rich models for 39TaxonSet_nuc It was found that homogeneous model 1GTR + 1C + 4had ln L = 62399033 whereas the heterogeneous model ofbest fit 2GTR + 4C + 4 had ln L =62094412 The BF anal-ysis shows very strong support for the use of the heteroge-neous model with 2(ln BF) = 609242 The posteriorpredictive simulations showed that the heterogeneousmodel 2GTR + 4C + 4 was able to accommodate the39TaxonSet_nuc data (P = 079) whereas the homogeneousmodel 1GTR + 1C + 4 was not (P = 000) (supplementaryfig S1 Supplementary Material online) We also foundstrong support for the application of heterogeneous modelsto the amino acid data set as 1JTT + 4 had lnL =22518425 and the heterogeneous model of best-fit1JTT + 5C + 4 scored ln L =22395476 This pro-vided strong support for the heterogeneous model with2(ln BF) = 24590 (supplementary table S3 SupplementaryMaterial online) Overall these BF analyses show very strongsupport for the application of heterogeneous modelsHowever posterior predictive simulations showed that for39TaxonSet_aa models allowing for compositional heteroge-neity across the phylogeny did not adequately describe thedata (P = 0000) (supplementary fig S1 SupplementaryMaterial online)

At this point in our analysis we wished to examine modelsthat vary across the phylogeny (NDRH and NDCH) to deter-mine whether they could further improve the fit of the modelto the data The application of GTR-like protein models in aphylogeny heterogeneous context is problematic (the 20character states implies too many parameters with each ad-ditional composition vector or exchange rate matrix) It wastherefore necessary to test the fit of NDRH-based modelsto the Dayhoff recoded data set 39TaxonSet_dayT

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BF comparisons between models that accommodate bothcompositional and exchange rate heterogeneity across thephylogeny 2GTR + 4C + 4 (ln L 10379131) and the ho-mogeneous model 1GTR + 1C + 4 (ln L 10396731) showstrong support for the heterogeneous model with 2(lnBF) = 3520 (supplementary table S3 SupplementaryMaterial online) The fit of the model (2GTR + 4C + 4) tothese data are supported by posterior predictive simulationswith a tail-area probability of 0004 (supplementary fig S1Supplementary Material online) Although this heteroge-neous model does not fit the data within the 95 confidenceinterval it is a far better descriptor of the data than the ho-mogeneous model 1GTR + 1C + 4 that 1) has a tail-areaprobability of 000 and 2) lies well outside the posteriordistribution of the simulated data set

We wished to determine which rooting position was sup-ported by the 39TaxonSet_nuc using the NDCH and NDRHmodels and the CAT-based models The 39TaxonSet_nuc wasassembled for this purpose and following cross validation weidentified CAT as a better fit to the data set however therewas very little support in favor of the Atlantogenata rooting(PP = 044) Posterior predictive simulations also showed thatthis data set is compositionally heterogeneous across theentire alignment (Z score = 1415) and across 2039 taxa (sup-plementary table S7 Supplementary Material online) Wetherefore tested the NDCH and NDRH models and identifiedthe 2GTR + 4C + 4 as the best fit Under the 2GTR +4C + 4 we found support for the Atlantogenata rootinghowever the 39TaxonSet_nuc data were unable to rejectthe competing hypotheses for the position of the root

The CAT and CAT-GTR models were applied to the39TaxonSet_aa to determine whether profile mixturemodels fit the data better than the 1GTR + 1C + 4 and1JTT + 4-based models The fit was again assessed using10-fold Bayesian cross validation The CAT-GTR model wasidentified through cross validation as a better fit to the39TaxonSet_aa At the amino acid level there were less com-positionally deviating taxa than the nucleotide level with only939 taxa having a Z scoregt 2 These nine compositionallyheterogeneous taxa (Z scoregt 2) are denoted in figure 3 Todetermine the effect of the observed compositional hetero-geneity on the phylogeny we 1) removed the offending taxaand repeated the phylogenetic analysis and 2) analyzed thedata at the Dayhoff category level (39TaxonSet_day) Withthe compositionally heterogeneous taxa removed the CAT-GTR model on the reduced 39TaxonSet_aa (amino acids)data set now passed the global test of compositional homo-geneity Analysis of the 39TaxonSet_day determined thatCAT-GTR was the best fit to the 39TaxonSet_day when com-pared with CAT and 1GTR + 1C + 4 models Posterior pre-dictive simulations of 39TaxaSet_day show that although theCAT-GTR model globally fits the data (Z score = 189) 10 taxaare compositionally heterogeneous (Z scoregt 2) As recodingthe 39TaxaSet_day data to Dayhoff categories did not ame-liorate compositional heterogeneity under the CAT-GTRmodel it was determined that the model that accountsfor compositional and exchange rate heterogeneity overthe phylogeny 2GTR + 4C + 4 better described the

39TaxonSet_day data set and the CAT-GTR model betterdescribed the 39TaxonSet_aa data set see summary table 3

Phylogenetic Results of Best Fitting Models on39TaxonSet

Support is found for the Atlantogenata position for the rootusing a model that accounts for compositional and exchangerate heterogeneity across the tree (2GTR + 4C + 4) on the39TaxonSet_nuc and 39TaxonSet_day data sets as well as amodel that accounts for heterogeneity across the data set(CAT-GTR) on 39Taxonset_day and 39Taxonset_aa datasets The branch leading to the common ancestor ofXenarthra and Afrotheria is the most strongly supported po-sition for the sister node to all other placental mammals seefigure 3 All topologies obtained are available for downloadfrom httpdxdoiorg105061dryadvh49j The removal ofcompositionally heterogeneous taxa (identified in39TaxonSet_aa through Posterior Predictive simulationsand highlighted in fig 3) does not alter the topology andthe Atlantogenata root remains the preferred position com-pared with the two alternative positions considered

We wished to assess whether the 39TaxonSet had thepower to reject alternative positions for the root AlthoughCAT-GTR is the best fitting model overall it is not feasible tocompare alternative topologies under CAT-based models em-ployed in PhyloBayes (Lartillot et al 2009) Therefore we usedthe best fitting P4 heterogeneous model 2GTR + 4C + 4(supports the Atlantogenata hypothesis with a PP value of100 for 39TaxonSet_nuc and PP value of 096 for39TaxonSet_day) Using the 2GTR + 4C + 4model the BFanalyses indicated that 39TaxonSet_day can discriminate be-tween alternative phylogenetic hypotheses with BF scores asfollows Afrotheria rooting 2ln (BF) = 1608 and Xenarthrarooting 2(ln BF) = 654 whereas the 39TaxonSet_nuc isunable to do so A recent study has shown that nucleotidedata are more susceptible to saturation at deeper nodes com-pared with amino acid data (Rota-Stabelli et al 2013) and thismay account for the lack of power in the nucleotide data setto distinguish between alternative hypotheses

The analysis of the 39TaxonSet_day data set shows thatalthough an improvement in fit of the model to the data set isobserved with the addition of compositional vectors and ex-change rate matrices support for Atlantogenata as the sistergroup of all the other placental mammals is not eroded withmore complex models

To test whether the NDCH and NDRH models are robustto poor taxon sampling we systematically reduced thenumber of representatives in each of the Afrotheria Xenar-thra Laurasiatheria and Euarchontoglires Superorders to asingle taxon and we reran the phylogenetic reconstructionanalyses using the heterogeneous models In the case of theEuarchontoglires we included the human and mouse inde-pendently as the representative taxon sampled In each ofthese reduced taxon sets the heterogeneous models(2GTR + 4C + 4 and CAT-GTR) recovered the Atlantogen-ata rooting hypothesis (see data online at the Dryad digitalrepository doi105061dryadvh49j)

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FIG 3 Mammal phylogeny reconstructed using two independent approaches Phylogeny reconstruction carried out using CAT-GTR model on the39TaxonSet_aa in PhlyoBayes v32 and on 39TaxonSet_day using the heterogeneous model 2GTR + 4C + 4 in P4 The support values for bothmethods are given at each node the numerator is the Bayesian support value for nodes based on CAT-GTR in PhyloBayes the denominator is thesupport value based on the 2GTR + 4C + 4 model in P4 Support values for the 2GTR + 4C + 4 model are shown where they are in agreement withthe CAT-GTR model topology The color scheme represents the major groups of mammals red Euarchontoglires blue Laurasiatheria green Afrotheriapurple Xenarthra gray Marsupials ( represents compositionally heterogeneous taxa) Gallus gallus and Ornithorhynchus anatinus were both compo-sitionally heterogeneous but were excluded in this figure to retain clarity of placental mammal branch lengths

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The phylogeny produced using the CAT-GTR model onthe 39TaxonSet_aa is almost completely congruent with thatobtained through the analysis of the 39TaxonSet_day usingthe 2GTR + 4C + 4 model (See fig 3) The only disagree-ment between these two topologies lies within theLaurasiatheria and relates to the placement of the fiveorders within this clade Our focus in this analysis wasthe resolution of the interordinal relationships within theplacental mammal phylogeny to address the intraorderplacement of the Laurasiatheria denser taxon samplingthrough sequencing of currently poorly represented ordersis necessary

ConclusionsWe set out to determine whether more sophisticated modelsthat account for compositional heterogeneity and exchangerate heterogeneity across both the phylogeny and the dataare required for the resolution of the placental root Analyzingpreviously published data and methods we have found thathomogeneous models and shorter alignments cannot fullyresolve this phylogenetic problem as they are not capableof rejecting all other competing hypotheses

In our 39TaxonSet we show that models that allow het-erogeneity across the data outperform those tested that allowfor heterogeneity across the phylogeny We illustrate that oursingle copy orthologous data set (39TaxonSet) is sufficientlylarge to accommodate these parameter rich modelsSignificantly these heterogeneous models achieve adequacyin modeling the evolutionary history of these sequences andthey are robust to taxonomic sampling We demonstrate thateven though heterogeneous NDRH and NDCH models fitnucleotide data better than homogeneous modes and givePP support values of 100 to the Atlantogenata hypothesisCAT-based models only support the Atlantogenata hypoth-esis with a PP = 044 and it is still not possible to distinguishbetween competing hypotheses using nucleotide data Theinability of the 39TaxonSet_nuc data set to reject the com-peting hypotheses for the position of the placental root is notsurprising Codon bias has been demonstrated at deep phy-logenetic positions (Rota-Stabelli et al 2013) and nucleotidedata will tend to saturate more than amino acid data Overallthese characteristics of the data provide a strong argumentfor the use of amino acids for deep-level phylogeny Strongstatistical support for the Atlantogenata hypothesis isachieved with the 39TaxonSet_day data set and heteroge-neous models and all major alternative hypotheses for theplacement of the root of placental mammals can be fullyrejected

As with any phylogenetic question congruence acrossmultiple independent data sets and independent analyses ishighly desirable The Atlantogenata hypothesis is being recov-ered with greater frequency in mammal phylogenetic analyses(Madsen et al 2001 Scally 2001 Hudelot et al 2003 vanRheede et al 2006 Nishihara et al 2007 Hallstrom et al2007 Wildman et al 2007 Hallstrom and Janke 2008Prasad et al 2008 Song et al 2012) Here we have shownthat when better fitting models are used support for theAtlantogenata hypothesis is increased The work presented

here demonstrates that the mammal phylogeny is knowableand that better phylogenetic modeling and the associatedlarger data requirements are of paramount importance toimproving our understanding of mammalian phylogenetics

Materials and Methods

Gene and Taxon Sampling

The first data set analyzed in the article was taken fromMurphy et al (2001b) and received no additional treatmentor editing of the alignment it is referred to as 66TaxonSetThis data set contains 66 species and 15 loci composed of 11coding 1 mtDNA and 3 30-UTR sequences totaling an align-ment length of 9789 bp The protein-coding portion of thesedata consists of 11 genes representing 2190 aligned aminoacid positions

The second data set was assembled specifically for thisanalysis using species available on the Ensembl server (version60) and the Polar Bear (Ursus maritimus) genome kindlysupplied freely online by the Beijing Genomics InstituteChina (Li et al 2011) this data set is referred to as the39TaxonSet The longest canonical transcripts for the 39taxa (38 mammals plus chicken) were analyzed using thebest reciprocal Blast hit approach (Hubbard et al 2005)One-to-one orthologs were identified using standaloneBlastP (Altschul et al 1990) with e6 and genes were assignedto the category of one-to-one ortholog following best recip-rocal Blast hit analysis and visual inspection of all alignmentsfor all families Only those single gene orthologs with highsequence quality across the entire gene length were retainedThis resulted in a total of 27 single gene orthologous familiesfor further analysis The careful curation of the data set en-sured accurate single gene ortholog identification despite thelow genome coverage (2) in 1939 of the species usedin this analysis The taxon coverage was between 6711 and9555 of the total concatenated nucleotide alignment(79884 nucleotides) Details of the 27 genes in the39TaxonSet used in this study including gene names acces-sion numbers alignment lengths GC content and composi-tion homogeneity on a gene and species level are given insupplementary table S5 Supplementary Material online

The 39TaxonSet alignment was manually edited usingthe Se-Al software (Rambaut 2001) sequencing errors andmisaligned regions were identified and removed The editedamino acid alignment was 27200 aa in length On removal ofinvariant sites the total alignment length for the 39TaxonSetwas 39275 bp or 11039 aa The Ensembl unique identifiers forall 27 genes are available in supplementary table S5 Supple-mentary Material online and the complete set of multiplesequence alignments are available at httpdxdoiorg105061dryadvh49j

Test for Compositional Homogeneity

The 66TaxonSet and the 39TaxonSet were tested for compo-sitional homogeneity by comparing the compositions of thetaxa with the mean composition of the data using the 2 testfor compositional homogeneity This test does not take thetopology of the taxa into account and as such cannot

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The phylogeny produced using the CAT-GTR model onthe 39TaxonSet_aa is almost completely congruent with thatobtained through the analysis of the 39TaxonSet_day usingthe 2GTR + 4C + 4 model (See fig 3) The only disagree-ment between these two topologies lies within theLaurasiatheria and relates to the placement of the fiveorders within this clade Our focus in this analysis wasthe resolution of the interordinal relationships within theplacental mammal phylogeny to address the intraorderplacement of the Laurasiatheria denser taxon samplingthrough sequencing of currently poorly represented ordersis necessary

ConclusionsWe set out to determine whether more sophisticated modelsthat account for compositional heterogeneity and exchangerate heterogeneity across both the phylogeny and the dataare required for the resolution of the placental root Analyzingpreviously published data and methods we have found thathomogeneous models and shorter alignments cannot fullyresolve this phylogenetic problem as they are not capableof rejecting all other competing hypotheses

In our 39TaxonSet we show that models that allow het-erogeneity across the data outperform those tested that allowfor heterogeneity across the phylogeny We illustrate that oursingle copy orthologous data set (39TaxonSet) is sufficientlylarge to accommodate these parameter rich modelsSignificantly these heterogeneous models achieve adequacyin modeling the evolutionary history of these sequences andthey are robust to taxonomic sampling We demonstrate thateven though heterogeneous NDRH and NDCH models fitnucleotide data better than homogeneous modes and givePP support values of 100 to the Atlantogenata hypothesisCAT-based models only support the Atlantogenata hypoth-esis with a PP = 044 and it is still not possible to distinguishbetween competing hypotheses using nucleotide data Theinability of the 39TaxonSet_nuc data set to reject the com-peting hypotheses for the position of the placental root is notsurprising Codon bias has been demonstrated at deep phy-logenetic positions (Rota-Stabelli et al 2013) and nucleotidedata will tend to saturate more than amino acid data Overallthese characteristics of the data provide a strong argumentfor the use of amino acids for deep-level phylogeny Strongstatistical support for the Atlantogenata hypothesis isachieved with the 39TaxonSet_day data set and heteroge-neous models and all major alternative hypotheses for theplacement of the root of placental mammals can be fullyrejected

As with any phylogenetic question congruence acrossmultiple independent data sets and independent analyses ishighly desirable The Atlantogenata hypothesis is being recov-ered with greater frequency in mammal phylogenetic analyses(Madsen et al 2001 Scally 2001 Hudelot et al 2003 vanRheede et al 2006 Nishihara et al 2007 Hallstrom et al2007 Wildman et al 2007 Hallstrom and Janke 2008Prasad et al 2008 Song et al 2012) Here we have shownthat when better fitting models are used support for theAtlantogenata hypothesis is increased The work presented

here demonstrates that the mammal phylogeny is knowableand that better phylogenetic modeling and the associatedlarger data requirements are of paramount importance toimproving our understanding of mammalian phylogenetics

Materials and Methods

Gene and Taxon Sampling

The first data set analyzed in the article was taken fromMurphy et al (2001b) and received no additional treatmentor editing of the alignment it is referred to as 66TaxonSetThis data set contains 66 species and 15 loci composed of 11coding 1 mtDNA and 3 30-UTR sequences totaling an align-ment length of 9789 bp The protein-coding portion of thesedata consists of 11 genes representing 2190 aligned aminoacid positions

The second data set was assembled specifically for thisanalysis using species available on the Ensembl server (version60) and the Polar Bear (Ursus maritimus) genome kindlysupplied freely online by the Beijing Genomics InstituteChina (Li et al 2011) this data set is referred to as the39TaxonSet The longest canonical transcripts for the 39taxa (38 mammals plus chicken) were analyzed using thebest reciprocal Blast hit approach (Hubbard et al 2005)One-to-one orthologs were identified using standaloneBlastP (Altschul et al 1990) with e6 and genes were assignedto the category of one-to-one ortholog following best recip-rocal Blast hit analysis and visual inspection of all alignmentsfor all families Only those single gene orthologs with highsequence quality across the entire gene length were retainedThis resulted in a total of 27 single gene orthologous familiesfor further analysis The careful curation of the data set en-sured accurate single gene ortholog identification despite thelow genome coverage (2) in 1939 of the species usedin this analysis The taxon coverage was between 6711 and9555 of the total concatenated nucleotide alignment(79884 nucleotides) Details of the 27 genes in the39TaxonSet used in this study including gene names acces-sion numbers alignment lengths GC content and composi-tion homogeneity on a gene and species level are given insupplementary table S5 Supplementary Material online

The 39TaxonSet alignment was manually edited usingthe Se-Al software (Rambaut 2001) sequencing errors andmisaligned regions were identified and removed The editedamino acid alignment was 27200 aa in length On removal ofinvariant sites the total alignment length for the 39TaxonSetwas 39275 bp or 11039 aa The Ensembl unique identifiers forall 27 genes are available in supplementary table S5 Supple-mentary Material online and the complete set of multiplesequence alignments are available at httpdxdoiorg105061dryadvh49j

Test for Compositional Homogeneity

The 66TaxonSet and the 39TaxonSet were tested for compo-sitional homogeneity by comparing the compositions of thetaxa with the mean composition of the data using the 2 testfor compositional homogeneity This test does not take thetopology of the taxa into account and as such cannot

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accommodate individual lineages that may deviate from theaverage compositional distribution (Rzhetsky and Nei 1995)Therefore we applied a second test for compositional homo-geneity that is a tree and model-based composition test(Foster 2004) We generated 100 simulated data sets basedon the given model phylogenetic tree and sample 2

m (wherem is the expected value from the model) The goodness of fitmeasure or 2-value from the actual data was then com-pared with the simulated data (2

m) With the topology fixedthe model parameters and branch lengths were optimized ina maximum likelihood framework and these data were con-sidered homogeneous if the calculated value is greater than95 of the null distribution (Foster 2004)

Model Selection

Substitution models were originally selected following modeltesting using ModelGenerator v85 (Keane et al 2006) Resul-tant models from this test were used as a starting point forthe generation of tree heterogeneous models The logarithmof the marginal likelihood was calculated using the NewtonRaftery equation 16 (Newton et al 1994) in P4 (Foster 2004)Through comparison of BFs we can identify the model thatfits the data best (while simultaneously not over fitting themodel to the data) Twice the difference in logarithms wascalculated and then compared with the Kass and Rafterytable (Kass and Raftery 1995)

The CAT and CAT-GTR were tested against homogeneousmodels in PhyloBayes v32 (Lartillot et al 2009) using BayesianCross Validation (Stone 1974) which is implemented inPhyloBayes v32 (Lartillot et al 2009) Cross Validation wascalculated by splitting the data set into two unequal parts thelearning set and the test set The parameters of the modelwere estimated on the learning set given the tree obtainedfrom the MCMC run The parameters were then used tocalculate the likelihood of the test set This was repeated 10times for each model and the average of the overall likelihoodscore was taken The scores from each model were thencompared and the top scoring model was chosen see sup-plementary table S7 Supplementary Material online for crossvalidation calculations

P4 Phylogenetic Analysis

The 66TaxonSet was analyzed at three levels 1) the nucleo-tide data set (66TaxonSet_nuc) 2) amino acid data set(66TaxonSet_aa) and 3) the recoded Dayhoff categories(66TaxonSet_day) For the 39TaxonSet heterogeneousmodels were applied at two levels 1) amino acids(39TaxonSet_aa) and 2) the recoded Dayhoff categories(39TaxonSet_day) The models applied to the data set areshown in supplementary table S2 Supplementary Materialonline For the 66TaxonSet the proportion of invariablesites (+I) and Gamma distributed associated rate variation(+) varied throughout the MCMC run to determine theoptimal values For the 39TaxonSet +I were removed andthe + value was optimized in P4 (Foster 2004) This estimatefor was then fixed for the remainder of the analyses Thisremoved the computational load required for optimizing

unnecessary parameters and therefore improved the speedof the analyses For each of the heterogeneous models tested10 independent MCMC runs were used and these were per-mitted to run long after the likelihood values of the chainconverged Only runs that had converged were used in fur-ther analyses this was determined by the agreement betweentopologies from the 10 independent MCMC runs and param-eters Topologies obtained from heterogeneous models in P4are available for download from httpdxdoiorg105061dryadvh49j

PhyloBayes Phylogenetic Analysis

Both the 66TaxonSet and the 39TaxonSet were analyzedusing CAT and CAT-GTR models along with homogeneousJTT + 4 and GTR + 1C + 4 (+I parameter added for66TaxonSet) in PhyloBayes v32 (Lartillot et al 2009) Foreach model tested two independent runs using fourMCMC chains were applied these were sampled every 100trees until convergence was reached see download httpdxdoiorg105061dryadvh49j

Posterior Predicative Simulations

Each model was tested for goodness-of-fit using posteriorpredictive simulations (Foster 2004) These tests were appliedto models that accommodate compositional heterogeneityacross the phylogeny in P4 (Foster 2004) and models thataccommodate heterogeneity across the data in PhyloBayesv32 (Lartillot et al 2009) Using the given model for a data setand the parameters estimated from the MCMC run for thatdata set simulated data sets were created The fit of themodel is estimated by the tail-area probability (Gelmanet al 1995) If the real data falls within the distribution ofthe simulated data set then the composition of the data iswell described by the given model Using posterior predictivesimulations in PhyloBayes v32 (Lartillot et al 2009) the com-positional deviation for each taxon along with the overall fitof the model to composition were assessed The output forposterior predictive simulations in PhyloBayes v32 (Lartillotet al 2009) were computed and given in the form of Z scoresA Z scoregt 2 indicated that the model failed to fit the datasee supplementary figure S1 and table S6 SupplementaryMaterial online for the 66TaxonSet and 39TaxonSet scoresrespectively

Supplementary MaterialSupplementary tables S1ndashS7 and figure S1 are available atMolecular Biology and Evolution online (httpwwwmbeoxfordjournalsorg)

Acknowledgments

CCM and AEW carried out single gene ortholog identifica-tion CCM carried out all data assembly phylogenetic andstatistical analyses CCM MJOrsquoC PGF DP and JOMcIcontributed to methodology phylogenetic tests and statisti-cal analyses MJOrsquoC conceived of the study its design andcoordination All authors participated in writing the manu-script The authors thank the Irish Research Council for

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Science Engineering and Technology (Embark InitiativePostgraduate Scholarship RS2000172 to CCM) MJOrsquoCand AEW are funded by Science Foundation IrelandResearch Frontiers Program EOB2673 and MJOrsquoC by the as-sociated SFI STTF-11 MJOrsquoC acknowledge the FulbrightCommission for the Fulbright Scholar Award 2012-2013CCM thanks the DCU Orla Benson Award 2011 for fundingand Roberto Feuda for assistance with PhyloBayes analysesThe authors thank the SFIHEA Irish Centre for High-EndComputing (ICHEC) and the SCI-SYM centre DCU for pro-cessor time and technical support The authors also thankthree anonymous reviewers for their comments that servedto greatly improve the quality and clarity of the manuscript

ReferencesAltschul SF Gish W Miller W Myers EW Lipman DJ 1990 Basic local

alignment search tool J Mol Biol 215403ndash410Asher RJ 2007 A web-database of mammalian morphology and a

reanalysis of placental phylogeny BMC Evol Biol 7108Bininda-Emonds OR Cardillo M Jones KE et al (10 co-authors) 2007

The delayed rise of present-day mammals Nature 446507ndash512Bleiweiss R 1998 Slow rate of molecular evolution in high-elevation

hummingbirds Proc Natl Acad Sci U S A 95612ndash616Brown WM Prager EM Wang A Wilson AC 1982 Mitochondrial DNA

sequences of primates tempo and mode of evolution J Mol Evol 18225ndash239

Caulin AF Maley CC 2011 Petorsquos paradox evolutionrsquos prescription forcancer prevention Trends Ecol Evol 26175ndash182

Cox CJ Foster PG Hirt RP Harris SR Embley TM 2008 The archae-bacterial origin of eukaryotes Proc Natl Acad Sci U S A 10520356ndash20361

de Magalhaes JP Costa J 2009 A database of vertebrate longevity re-cords and their relation to other life-history traits J Evol Biol 221770ndash1774

Dorus S Evans PD Wyckoff GJ Choi SS Lahn BT 2004 Rate of molecularevolution of the seminal protein gene SEMG2 correlates with levelsof female promiscuity Nat Genet 361326ndash1329

Foster PG 2004 Modeling compositional heterogeneity Syst Biol 53485ndash495

Foster PG Cox CJ Embley TM 2009 The primary divisions of life aphylogenomic approach employing composition-heterogeneousmethods Philos Trans R Soc Lond B Biol Sci 3642197ndash2207

Foster PG Hickey DA 1999 Compositional bias may affect both DNA-based and protein-based phylogenetic reconstructions J Mol Evol48284ndash290

Galtier N 2003 Gene conversion drives GC content evolution in mam-malian histones Trends Genet 1965ndash68

Gelman AB Carlin JS Stern HS Rubin DB 1995 Bayesian data analysisLondon Chapman amp Hall

Hallstrom BM Janke A 2008 Resolution among major placentalmammal interordinal relationships with genome data imply thatspeciation influenced their earliest radiations BMC Evol Biol 8162

Hallstrom BM Janke A 2010 Mammalian evolution may not be strictlybifurcating Mol Biol Evol 272804ndash2816

Hallstrom BM Kullberg M Nilsson MA Janke A 2007 Phylogenomicdata analyses provide evidence that Xenarthra and Afrotheria aresister groups Mol Biol Evol 242059ndash2068

Han KL Braun EL Kimball RT et al (17 co-authors) 2011 Are trans-posable element insertions homoplasy free an examination usingthe avian tree of life Syst Biol 60375ndash386

Heath TA Zwickl DJ Kim J Hillis DM 2008 Taxon sampling affectsinferences of macroevolutionary processes from phylogenetic treesSyst Biol 57160ndash166

Holton TA Pisani D 2010 Deep genomic-scale analyses of the metazoareject Coelomata evidence from single- and multigene families

analyzed under a supertree and supermatrix paradigm GenomeBiol Evol 2310ndash324

Hrdy I Hirt RP Dolezal P Bardonova L Foster PG Tachezy J Embley TM2004 Trichomonas hydrogenosomes contain the NADH dehydro-genase module of mitochondrial complex I Nature 432618ndash622

Hubbard T Andrews D Caccamo M et al (52 co-authors) 2005Ensembl 2005 Nucleic Acids Res 33D447ndashD453

Hudelot C Gowri-Shankar V Jow H Rattray M Higgs PG 2003 RNA-based phylogenetic methods application to mammalian mitochon-drial RNA sequences Mol Phylogenet Evol 28241ndash252

Janecka JE Miller W Pringle TH Wiens F Zitzmann A Helgen KMSpringer MS Murphy WJ 2007 Molecular and genomic data iden-tify the closest living relative of primates Science 318792ndash794

Kass RE Raftery AE 1995 Bayes factors J Am Stat Assoc 90773ndash795Keane TM Creevey CJ Pentony MM Naughton TJ McInerney JO 2006

Assessment of methods for amino acid matrix selection and theiruse on empirical data shows that ad hoc assumptions for choice ofmatrix are not justified BMC Evol Biol 629

Kosiol C Holmes I Goldman N 2007 An empirical codon model forprotein sequence evolution Mol Biol Evol 241464ndash1479

Kriegs JO Churakov G Kiefmann M Jordan U Brosius J Schmitz J 2006Retroposed elements as archives for the evolutionary history ofplacental mammals PLoS Biol 4e91

Lartillot N Lepage T Blanquart S 2009 PhyloBayes 3 a Bayesian soft-ware package for phylogenetic reconstruction and molecular datingBioinformatics 252286ndash2288

Lartillot N Philippe H 2004 A Bayesian mixture model for across-siteheterogeneities in the amino-acid replacement process Mol BiolEvol 211095ndash1109

Leroi AM Koufopanou V Burt A 2003 Cancer selection Nat RevCancer 3226ndash231

Li B Zhang G Willersleve E Wang J Wang J 2011 Genomic data fromthe polar bear (Ursus maritimus) [Internet] GigaScience [cited2013 July 23] Available from httpdxdoiorg105524100008

Li WH Ellsworth DL Krushkal J Chang BH Hewett-Emmett D 1996Rates of nucleotide substitution in primates and rodents and thegeneration-time effect hypothesis Mol Phylogenet Evol 5182ndash187

Li WH Tanimura M Sharp PM 1987 An evaluation of the molecularclock hypothesis using mammalian DNA sequences J Mol Evol 25330ndash342

Madsen O Scally M Douady CJ et al (10 co-authors) 2001 Paralleladaptive radiations in two major clades of placental mammalsNature 409610ndash614

Martin AP Palumbi SR 1993 Body size metabolic rate generationtime and the molecular clock Proc Natl Acad Sci U S A 904087ndash4091

McCormack JE Faircloth BC Crawford NG Gowaty PA Brumfield RTGlenn TC 2012 Ultraconserved elements are novel phylogenomicmarkers that resolve placental mammal phylogeny when combinedwith species-tree analysis Genome Res 22746ndash754

Meredith RW Janecka JE Gatesy J et al (22 co-authors) 2011 Impactsof the cretaceous terrestrial revolution and KPg extinction onmammal diversification Science 334521ndash524

Moreira D Philippe H 2000 Molecular phylogeny pitfalls and progressInt Microbiol 39ndash16

Murphy WJ Eizirik E Johnson WE Zhang YP Ryder OA OrsquoBrien SJ2001a Molecular phylogenetics and the origins of placental mam-mals Nature 409614ndash618

Murphy WJ Eizirik E OrsquoBrien SJ et al (11 co-authors) 2001b Resolutionof the early placental mammal radiation using Bayesian phyloge-netics Science 2942348ndash2351

Murphy WJ Pringle TH Crider TA Springer MS Miller W 2007 Usinggenomic data to unravel the root of the placental mammal phy-logeny Genome Res 17413ndash421

Newton MA Raftery AE Davison AC et al (34 co-authors) 1994Approximate Bayesian-inference with the weighted likelihood boot-strap J R Stat Soc B Methodol 563ndash48

Nishihara H Maruyama S Okada N 2009 Retroposon analysis andrecent geological data suggest near-simultaneous divergence of

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the three superorders of mammals Proc Natl Acad Sci U S A 1065235ndash5240

Nishihara H Okada N Hasegawa M 2007 Rooting the eutherian treethe power and pitfalls of phylogenomics Genome Biol 8R199

OrsquoLeary MA Bloch JI Flynn JJ et al (23 co-authors) 2013 The placentalmammal ancestor and the post-K-Pg radiation of placentals Science339662ndash667

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Philippe H Brinkmann H Lavrov DV Littlewood DT Manuel MWorheide G Baurain D 2011 Resolving difficult phylogenetic ques-tions why more sequences are not enough PLoS Biol 9e1000602

Prasad AB Allard MW Green ED 2008 Confirming the phylogeny ofmammals by use of large comparative sequence data sets Mol BiolEvol 251795ndash1808

Quang LS Gascuel O Lartillot N 2008 Empirical proEle mixture modelsfor phylogenetic reconstruction Bioinformatics 242317ndash2323

Rambaut A 2001 Se-AL [Internet] [cited 2009 Dec 2] Available fromhttptreebioedacuksoftwareseal

Rokas A Kruger D Carroll SB 2005 Animal evolution and the molec-ular signature of radiations compressed in time Science 3101933ndash1938

Romiguier J Ranwez V Douzery EJ Galtier N 2010 Contrasting GC-content dynamics across 33 mammalian genomes relationship withlife-history traits and chromosome sizes Genome Res 201001ndash1009

Rota-Stabelli O Lartillot N Philippe H Pisani D 2013 Serine codonusage bias in deep phylogenomics pancrustacean relationships asa case study Syst Biol 62121ndash133

Rzhetsky A Nei M 1995 Tests of applicability of several substitutionmodels for DNA sequence data Mol Biol Evol 12131ndash151

Scally M 2001 Molecular evidence for the major clades of placentalmammals J Mammal Evol 8239ndash277

Song S Liu L Edwards SV Wu S 2012 Resolving conflict ineutherian mammal phylogeny using phylogenomics and the multi-species coalescent model Proc Natl Acad Sci U S A 10914942ndash14947

Stanhope MJ Waddell VG Madsen O de Jong W Hedges SB ClevenGC Kao D Springer MS 1998 Molecular evidence for multipleorigins of Insectivora and for a new order of endemic African insec-tivore mammals Proc Natl Acad Sci U S A 959967ndash9972

Stone M 1974 Cross-validatory choice and assessment of statisticalpredictions J R Stat Soc 36111ndash147

Usanga EA Luzzatto L 1985 Adaptation of Plasmodium falciparum toglucose 6-phosphate dehydrogenase-deficient host red cells by pro-duction of parasite-encoded enzyme Nature 313793ndash795

van Rheede T Bastiaans T Boone DN Hedges SB de Jong WW MadsenO 2006 The platypus is in its place nuclear genes and indels con-firm the sister group relation of monotremes and Therians Mol BiolEvol 23587ndash597

Waddell PJ Cao Y Hauf J Hasegawa M 1999 Using novel phylogeneticmethods to evaluate mammalian mtDNA including amino acid-invariant sites-LogDet plus site stripping to detect internal conflictsin the data with special reference to the positions of hedgehogarmadillo and elephant Syst Biol 4831ndash53

Wildman DE Uddin M Opazo JC et al (10 co-authors) 2007Genomics biogeography and the diversification of placental mam-mals Proc Natl Acad Sci U S A 10414395ndash14400

Yang Z 1998 Likelihood ratio tests for detecting positive selection andapplication to primate lysozyme evolution Mol Biol Evol 15568ndash573

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