# Drift and Genome Complexity Revisited

article has not abstract

Published in the journal:
. PLoS Genet 7(6): e32767. doi:10.1371/journal.pgen.1002092

Category:
Viewpoints

doi: 10.1371/journal.pgen.1002092

## Summary

article has not abstract

## Introduction

Recently, Whitney and Garland *[1]* (hereafter “WG”) reanalyzed a dataset
presented in Lynch and Conery *[2]* (hereafter “LC”) using phylogenetic
statistical techniques. Contrary to LC, WG found little support for the idea that
*N _{e}u* (the product of effective population size
and the mutation rate) is statistically related to genome size or six other genomic
attributes. Lynch

*[3]*has responded with criticisms of the WG approach and interpretations. Below we carefully consider these criticisms, present additional analyses, and conclude that the WG analyses are robust. In addition, we explore the consistency of some predictions of the mutational-hazard (MH) hypothesis

*[3]*and provide some guidance regarding future tests.

Given that both analyses used the same dataset, the heart of the issue is the choice
of analysis techniques and interpretation of results. Below, we use the terms
“phylogenetic” and “nonphylogenetic” to describe the
techniques employed by WG and LC, respectively. “Nonphylogenetic”
remains in quotes because, in fact, species-level regression or correlation analyses
that do not explicitly incorporate phylogenetic history do assume a particular
phylogeny—a star phylogeny (polytomy) in which all species are equally related
and all branches have equal lengths *[4]*, *[5]* .

## The Appropriateness of Phylogenetic Analyses

Lynch *[3]* argues
that both *N _{e}u* and measures of genome complexity (e.g.,
genome size) are so evolutionarily labile that analyses incorporating a hierarchical
phylogenetic tree are unnecessary and potentially misleading (but see

*[6]*). The issue can be empirically addressed

*[7]*,

*[8]*. The key test of whether a phylogenetic or “nonphylogenetic” regression analysis is more appropriate examines the regression residuals for phylogenetic signal

*[8]*,

*[9]*. Phylogenetic signal in the residuals is evidence that the evolutionary response of the dependent variable to the independent variable was not so rapid as to make phylogeny unimportant in regression analyses. This was the agnostic approach taken in WG, letting the statistics indicate the best-fit model. The phylogenetic models had better fit (see

*Table 1*in

*[1]*), indicating significant phylogenetic signal in the residuals. These models did not support the hypothesis that

*N*explains a significant fraction of the variation in genomic attributes such as genome size.

_{e}u

Although the key insight regarding trait lability is determined from the phylogenetic
signal of the regression residuals, it can also be instructive to examine
phylogenetic signal for particular traits. *Table 1* presents estimates of phylogenetic signal
(K) for the dataset under discussion; all traits show significant (and often
extremely strong) phylogenetic signal, indicating that species cannot be considered
statistically independent entities for any of these traits *[7]*. Such strong phylogenetic
signal may be counterintuitive for *N _{e}u,* which is a
population-level trait as opposed to a “standard” individual-level
morphological trait. However,

*N*can be construed as an emergent trait that reflects several other traits (e.g., mating system, dispersal ability, social group size, body size) that generally do show phylogenetic signal (e.g.,

_{e}*[7]*). In any case, the empirical data do not support Lynch's contention that

*N*(as estimated by π

_{e}u_{s,}the average nucleotide heterozygosity at silent sites) is so labile as to “hav[e] no shared phylogenetic history” across the species in the dataset.

Next, Lynch argues that phylogenetic techniques are inappropriate for the current
dataset because “. . . phylogenetic inertia is overshadowed by other
evolutionary effects. For example, for the two most closely related species . . .
mouse and human . . . numerous shared features of genome architecture are a
consequence of convergent evolution, not shared ancestry.” He observes that
genome sizes in different species may be determined by the abundances of different
transposable element (TE) families. Although it is certainly true that genome
architecture can be superficially similar because of convergent evolution, and that
such convergence can evolve via different underlying components (e.g., different TEs
in the case of genome size), these observations do not automatically override the
necessity for phylogenetic analyses. Phylogenetic nonindependence must be accounted
for if it exists, no matter how it arises. Phylogenetic signal in the residuals of
the regression of genome size on *N _{e}u* (see WG and

*Table 2*of the current article) indicates that related species could share similar values of other traits (aside from

*N*) that influence genome size. We posit that traits influencing the proliferation of TEs (e.g., mating system, methylation propensity, RNAi-mediated interference) show phylogenetic signal and are partly responsible for the nonindependence observed among residual genome sizes of closely related species. Another non-mutually-exclusive hypothesis is that related taxa share physiological traits that partly determine the environments in which they can live (e.g.,

_{e}u*[10]*,

*[11]*), and that the resulting shared environmental conditions have caused selection favoring similar-sized genomes. Regardless of one's ability to identify the lower-level traits involved, phylogenetic nonindependence of residuals is present in the current dataset (WG and

*Table 2*of the current article), and ignoring it can lead to incorrect inferences about associations between traits.

Finally, Lynch makes two general criticisms of phylogenetic methods. First, he
asserts “it can be shown” that the phylogenetically independent contrast
method inflates the sampling variance of the independent variable and decreases
*r*^{2} values by ≈30%. No justification or
citation is given for this assertion, and we know of no such bias. Moreover,
*r*^{2} values are generally not directly comparable
across “nonphylogenetic” and phylogenetic regression models *[9]*. Second, citing
*[12]*, Lynch
states that ordinary least-squares (OLS) correlations are “on average,
unbiased” and that similar correlations are expected “whether or not
shared phylogenetic history is accounted for.” Indeed, empirically, parameter
estimates from the two types of analyses are often similar (see also *[5]*, *[13]*). However, this
*average* outcome across studies does not prevent phylogenetic
versus “nonphylogenetic” analyses from giving very different answers for
a particular dataset, which is clearly the case here. Thus, any conclusion that a
“nonphylogenetic” analysis will always provide the correct inference is
not warranted.

## Estimation of *N*_{e}u

_{e}u

Lynch identifies three issues relating to *N _{e}u* and to
estimating

*N*via π

_{e}u_{s}: 1) estimates of π

_{s}are associated with high sampling variance; 2) because of constraints on

*N*and

_{e}*u*, many prokaryote species will have similar

*N*values; and 3) π

_{e}u_{s}in unicellular species is subject to downward bias resulting from selection on silent sites, perhaps causing prokaryotic

*N*estimates to be off by more than an order of magnitude. These issues are properly viewed as criticisms of the dataset itself, not the chosen analysis. They are equally applicable to the OLS analysis of LC and have no bearing on whether a phylogenetic versus “nonphylogenetic” analysis is more appropriate.

_{e}uWe note that error in the independent variable can be incorporated into both
phylogenetic and “nonphylogenetic” regression analyses using special
techniques (e.g., *[14]*). However, such techniques require that the error be
quantified. For the current dataset, error in π_{s} is not quantified,
and thus neither we nor Lynch have the opportunity to apply such techniques.

## Tree Topologies and Branch Lengths

Lynch argues that potential uncertainties associated with tree topology and branch
lengths weaken the conclusions of WG. We agree that errors in topologies and branch
lengths can influence the outcomes of phylogenetically based statistical analyses
*[4]*, *[5]*, *[15]*. However,
the key point is that a “nonphylogenetic” analysis (e.g., the OLS
regression performed in LC) is not phylogeny-free. Regression analyses assume that
residuals in the dependent (Y) variable are independent and identically distributed.
Under Brownian-motion-like evolution, the only phylogenetic tree that generates the
appropriate variance–covariance matrix (an identity matrix) is a star
phylogeny, in which each taxon is equally related to all other taxa and branch
lengths are equal *[4]*, *[5]*. In effect, the LC analysis assumes that humans are no
more closely related to mice than to bacteria. Clearly, if there are critical errors
in tree topology (and branch lengths) that undermine the conclusions of the
alternate analyses under discussion here, then they are found in the star phylogeny
assumed by LC.

The sensitivity of a phylogenetic comparative analysis is often assessed by examining
alternative topologies and/or branch lengths (e.g., *[16]*). To assess the robustness of
the WG results, we have investigated a second topology suggested by Lynch *[3]* and two
additional sets of branch lengths. The WG topology followed the “Coelomata
hypothesis,” whereas the alternate topology reflects the “Ecdysozoa
hypothesis” and unites nematodes and arthropods in a monophyletic group *[17]*. We did not
investigate a third topology suggested by Lynch, as it is not supported in recent
analyses *[18]*–*[20]*. Three sets of branch lengths were calculated for the two
trees: arbitrary lengths (all = 1) as in WG, lengths derived
from fossil-based divergence times, and lengths based on ribosomal RNA
substitutions. Full methodological details are available as supplementary material
from the Rice Digital Scholarship Archive at http://hdl.handle.net/1911/61373. Consistent with the WG results,
none of the six phylogenetic generalized least-squares (PGLS) analyses found
statistically significant relationships between *N _{e}u* and
genome size, and the models using all = 1 branch lengths best
fit the data (had the highest likelihoods) regardless of the topology (

*Table 2*). Thus, the conclusion of no relationship between

*N*and genome size appears robust to substantial variation in topologies and branch lengths.

_{e}uThe analyses of topologies and branch lengths described above (including the star
topology assumed by OLS) all assume a Brownian motion–like model of residual
trait evolution. If residual evolution has not been Brownian motion–like, then
both PGLS and OLS analyses may be suspect. This is why WG explored an additional
model—the Ornstein-Uhlenbeck (OU) model, which is based on a diffusion process
in which a particle wanders via a random walk, but is bounded by a restraining force
whose power increases with distance from the starting point *[7]*, *[21]*. Felsenstein (*[21]*, p. 464)
argued that the OU process is a good model for “the motion of a population
which is wandering back and forth on a selective peak under the influence of genetic
drift” or for “the wanderings of an adaptive peak in the phenotype
space.” WG verified that a regression model with residuals modeled as an OU
process (RegOU; *[9]*) fit significantly better than OLS, and found that it also did
not support a relationship between *N _{e}u* and genome size.
We have expanded those results by examining RegOU models for the full set of
topologies and branch lengths (

*Table 2*). Again, the best-fitting models for both topologies had starter branch lengths of 1.0 and did not support a significant relationship between

*N*and genome size (

_{e}u*Table 2*).

## Thresholds

Lynch *[3]* states
that the MH hypothesis predicts threshold (nonlinear) relationships on a log scale
between *N _{e}u* and measures of genome complexity, including
genome size. Therefore, he argues that the WG analyses of linear relationships are
inherently flawed. We find this argument inconsistent, given that a central analysis
of LC examines the relationship between log

*N*and log genome size and reports a highly significant

_{e}u*linear*relationship (

*r*

^{2}= 0.66; their Figure 1b). Furthermore, neither LC nor

*[22]*discuss thresholds or nonlinearity in the

*N*/ genome size relationship, nor is there obvious visual evidence of thresholds in the data (Figure 1b of

_{e}u*[2]*; Figure 4.8 of

*[22]*; Figure 3a of

*[1]*). As with genome size, three of the remaining six attributes analyzed in WG (gene number, the half-life of gene duplicates, and intron size) are clearly not associated with thresholds in LC, given that they are presented as linear relationships or, in the case of gene number, a slightly curvilinear relationship (see Figures 1–3 of

*[2]*).

WG did perhaps err in conducting linear analyses of *N _{e}u*
against three other genomic attributes associated with thresholds in LC: intron
number, transposon number, and transposon fraction. However, Lynch's argument
that a “substantial reduction in the correlation of
[

*N*with] genomic attributes” does not contradict the MH hypothesis but instead follows from WG's use of phylogenetic techniques is not correct: the problem is not that WG used PGLS, but that within PGLS, they chose to model linear rather than threshold relationships for these particular attributes. PGLS is capable of modeling any relationship possible with OLS

_{e}u*[23]*, including linear, polynomial, and break-point relationships (e.g., segmented regression

*[24]*).

A simple approach to test for threshold effects of *N _{e}u* is
via the PGLS equivalent of ANCOVA

*[9]*on two groups separated into low versus high

*N*. Of the 15 species with

_{e}u*N*and intron number data in the LC dataset, only two fall into the “high”

_{e}u*N*class (

_{e}u*N*>0.015); similarly, of the 18 species with transposon number (or fraction) data, only three fall into the “high”

_{e}u*N*class (

_{e}u*N*>0.0128). These highly unbalanced designs do not allow confidence in analysis via either regular or phylogenetic ANCOVA. Therefore, the LC dataset does not permit robust conclusions about the responses of introns and transposons to

_{e}u*N*thresholds, regardless of whether one utilizes phylogenetic or “nonphylogenetic” techniques.

_{e}u## Lessons from Other Studies

Lynch takes issue with WG's interpretations of two other studies. In both cases,
he argues that the metric used to estimate the strength of drift/selection
(allozyme-derived *N _{e}*

*[25]*;

*K*

_{a}/K_{s}*[26]*) is inappropriate for investigating relationships between drift and genome complexity. We argue below that allozyme-derived

*N*is in fact informative for the dataset in

_{e}*[25]*. The merits of

*K*have been discussed elsewhere

_{a}/K_{s}*[26]*–

*[28]*and will not be treated further here. Despite concerns about the

*K*metric, Lynch

_{a}/K_{s}*[3]*nonetheless views the results in bacteria

*[26]*as “compelling support” for the MH hypothesis.

Whitney et al. *[25]* examined allozyme-based estimates of
*N _{e}* and genome size for 205 species of seed
plants; using phylogenetically independent contrasts, no significant relationship
was detected. (OLS analysis found a significant negative relationship, apparently
the basis of Lynch's characterization of the results as
“consistent” with the MH hypothesis.) Lynch argues first that allozyme
data are not useful for estimating

*N*, because allozymes are products of protein-sequence variation and thus are less reliable surrogates of neutral variation than silent sites. We agree that there are likely constraints on allozyme

_{e}u*H*that limit the maximum

*N*that can be estimated; however, it does not follow that the signal of

_{e}u*N*is completely erased. In fact, as discussed in

_{e}u*[25]*, a significant positive correlation exists between allozyme-based and sequence-based

*N*estimates in a subset of the plant dataset. Furthermore, for a subset of the LC dataset for which allozyme data were available, allozyme-based

_{e}u*N*was as strongly related to genome size as was sequence-based

_{e}u*N*

_{e}u*[25]*. Lynch also argues that regressions in

*[25]*should have used

*N*rather than

_{e}u*N*. In that analysis,

_{e}*N*was calculated from heterozygosity

_{e}*H*via

*N*= ((1–

_{e}*H*)

^{−2}–1)

*/*(8

*u*), assuming a constant

*u*of 10

^{−5}. That assumption means that, computationally, it makes absolutely no difference whether

*N*or

_{e}u*N*were used; neither had a significant relationship with genome size in phylogenetic analyses.

_{e}Kuo et al. *[26]*
analyzed 42 paired bacterial genomes, using the efficacy of purifying selection in
coding regions (as estimated by *K _{a}/K_{s}*) to
quantify genetic drift. Bacterial taxa experiencing greater levels of genetic
drift—implying a smaller evolutionary

*N*—had smaller genomes. Lynch

_{e}*[3]*argues that these results support the MH hypothesis because “the theory predicts that with increasing power of random genetic drift, effectively neutral genomic features will evolve in the direction of mutation bias” and because “there is a deletion bias in bacteria” in contrast to an insertion bias in eukaryotes. Thus, the predicted

*N*and genome size/complexity relationship is positive for prokaryotes and negative for eukaryotes. These statements appear to represent a revision of the MH hypothesis, which in previous treatments

_{e}u*[2]*,

*[22]*had assumed an insertion bias in both groups and a continuous, negative

*N*versus genome size relationship across prokaryotes and eukaryotes.

_{e}uThe assertion that mutation bias differs in direction for prokaryotes and eukaryotes
is difficult to evaluate. We note that studies examining mutation bias typically
find a deletion bias in both groups (e.g., *[29]* and references therein). More
importantly, most of these studies use sequence data from diverged lineages to
estimate the ratio of insertions to deletions. In previous discussions, Lynch has
argued *[22]*, *[30]* that such
studies do not accurately estimate the quantity of interest (de novo mutation bias),
in contrast to lab mutation accumulation studies involving relaxation of selection.
We agree: indels in sequence data from naturally diverged lineages reflect not only
mutation but also subsequent selection and drift and thus may not represent the de
novo mutation spectrum. However, lab mutation accumulation studies *[31]*, *[32]* are simply too
few to allow generalizations about mutation biases in prokaryotes versus eukaryotes.
The lack of hard data on de novo mutation bias means that any nonzero correlation
between *N _{e}u* and genome size can be judged
“consistent” with the MH hypothesis simply by claiming the appropriate
mutation bias.

Regardless, the new prediction for decreasing prokaryotic genome size with decreasing
*N _{e}u* is not supported by the LC dataset, whether
analyzed using “nonphylogenetic” or phylogenetic methods. We regressed
genome size on

*N*using both OLS and PGLS for just the seven bacterial species and found no statistical relationship in either analysis (

_{e}u*b*= −0.19 and −0.11,

*P*= 0.47 and 0.49, respectively). Although the sample size is small, we note the trends are for genome size and

*N*to move in opposite directions, counter to the prediction if a deletion bias in bacteria is assumed.

_{e}uIn summary, the datasets of Whitney et al. *[25]* and of LC do not support the
MH hypothesis regardless of the assumed direction of mutation bias. The Kuo et al.
data *[26]* contradict
the MH hypothesis, assuming a universal insertion bias, but support it under an
assumption of a deletion bias in prokaryotes. We conclude, as did WG, that current
comparative datasets examining drift and genome size provide little support for the
MH hypothesis.

## Conclusions

We agree with Lynch *[3]* that the MH hypothesis should not be rejected based on the
difficulty of performing formal hypothesis tests. We note, however, that such
difficulty does not in turn justify acceptance based on inappropriate statistical
models. We find the theoretical population genetic basis of the original LC argument
sound: smaller effective population size should result in an increasing role for
drift relative to selection and an increasing probability of fixation of slightly
deleterious mutations that alter genome size and complexity. Our focus, however, is
not whether effective population size plays a role, but how important it might be
relative to numerous other factors that might influence genome size and complexity.
Does *N _{e}u* explain 66% of the variation in genome
size across the tree of life, 6%, or 0.6%? The WG analysis and those
presented herein suggest that, given the demonstrated phylogenetic nonindependence
of the data at hand, the 66% estimate claimed by LC is far too high; in fact,
any influence of

*N*on genome size is not statistically detectable in better-fitting phylogenetic regression models (

_{e}u*Table 2*). Finally, we question whether simple regression models (regardless of whether they are phylogenetic or “nonphylogenetic”) can ever provide unequivocal support for the MH hypothesis. One of the major criticisms expressed in WG and in

*[33]*is that

*N*is highly correlated with other aspects of organismal biology, including body size, mating system, developmental rate, and metabolic rate. Thus, comparative analyses using only

_{e}u*N*as a predictor variable may be uninformative about the actual mechanisms driving genome size and complexity; multivariate analyses are needed.

_{e}u##### Zdroje

1. WhitneyKDGarlandTJr

2010

Did genetic drift drive increases in genome

complexity?

PLoS Genet

6

e1001080

doi:10.1371/journal.pgen.1001080

2. LynchMConeryJS

2003

The origins of genome complexity.

Science

302

1401

1404

3. LynchM

2011

Statistical inference on the mechanisms of genome

evolution.

PLoS Genet

7

e1001389. doi: 10.1371/journal.pgen.1001389

4. GarlandTJrBennettAFRezendeEL

2005

Phylogenetic approaches in comparative

physiology.

J Exp Biol

208

3015

3035

5. GarlandTMidfordPEIvesAR

1999

An introduction to phylogenetically based statistical methods,

with a new method for confidence intervals on ancestral

values.

Am Zool

39

374

388

6. LynchMConeryJS

2004b

Testing genome complexity - response.

Science

304

390

7. BlombergSPGarlandTIvesAR

2003

Testing for phylogenetic signal in comparative data: Behavioral

traits are more labile.

Evolution

57

717

745

8. FreckletonRPHarveyPHPagelM

2002

Phylogenetic analysis and comparative data: A test and review of

evidence.

Am Nat

160

712

726

9. LavinSRKarasovWHIvesARMiddletonKMGarlandT

2008

Morphometrics of the avian small intestine compared with that of

nonflying mammals: A phylogenetic approach.

Physiol Zool

81

526

550

10. HueyRBDeutschCATewksburyJJVittLJHertzPE

2009

Why tropical forest lizards are vulnerable to climate

warming.

Proc Biol Sci

276

1939

1948

11. SwansonDLGarlandT

2009

The evolution of high summit metabolism and cold tolerance in

birds and its impact on present-day distributions.

Evolution

63

184

194

12. RicklefsREStarckJM

1996

Applications of phylogenetically independent contrasts: A mixed

progress report.

Oikos

77

167

172

13. RohlfFJ

2006

A comment on phylogenetic correction.

Evolution

60

1509

1515

14. IvesARMidfordPEGarlandT

2007

Within-species variation and measurement error in phylogenetic

comparative methods.

Syst Biol

56

252

270

15. Diaz-UriarteRGarlandT

1998

Effects of branch length errors on the performance of

phylogenetically independent contrasts.

Syst Biol

47

654

672

16. HutcheonJMGarlandT

2004

Are megabats big?

J Mammal Evol

11

257

276

17. AdoutteABalavoineGLartillotNLespinetOPrud'hommeB

2000

The new animal phylogeny: Reliability and

implications.

Proc Natl Acad Sci U S A

97

4453

4456

18. DelsucFBrinkmannHChourroutDPhilippeH

2006

Tunicates and not cephalochordates are the closest living

relatives of vertebrates.

Nature

439

965

968

19. DunnCWHejnolAMatusDQPangKBrowneWE

2008

Broad phylogenomic sampling improves resolution of the animal

tree of life.

Nature

452

745-U745

20. PhilippeHDerelleRLopezPPickKBorchielliniC

2009

Phylogenomics revives traditional views on deep animal

relationships.

Curr Biol

19

706

712

21. FelsensteinJ

1988

Phylogenies and quantitative characters.

Annu Rev Ecol Syst

19

445

471

22. LynchM

2007

The origins of genome architecture.

Sunderland (Massachusetts)

Sinauer Associates

23. GarlandTIvesAR

2000

Using the past to predict the present: Confidence intervals for

regression equations in phylogenetic comparative methods.

Am Nat

155

346

364

24. ChappellR

1989

Fitting bent lines to data, with applications to

allometry.

J Theor Biol

138

235

256

25. WhitneyKDBaackEJHamrickJLGodtMJWBarringerBC

2010

A role for nonadaptive processes in plant genome size

evolution?

Evolution

64

2097

2109

26. KuoCHMoranNAOchmanH

2009

The consequences of genetic drift for bacterial genome

complexity.

Genome Res

19

1450

1454

27. DaubinVMoranNA

2004

Comment on "The origins of genome complexity".

Science

306

978a

28. YangZHBielawskiJP

2000

Statistical methods for detecting molecular

adaptation.

Trends Ecol Evol

15

496

503

29. KuoCHOchmanH

2009

Deletional bias across the three domains of life.

Genome Biol Evol

1

145

152

30. LynchMConeryJS

2004a

Response to comment on "The origins of genome

complexity".

Science

306

978a

31. DenverDRMorrisKLynchMThomasWK

2004

High mutation rate and predominance of insertions in the

Caenorhabditis elegans nuclear genome.

Nature

430

679

682

32. NilssonAIKoskiniemiSErikssonSKugelbergEHintonJCD

2005

Bacterial genome size reduction by experimental

evolution.

Proc Natl Acad Sci U S A

102

12112

12116

33. CharlesworthBBartonN

2004

Genome size: Does bigger mean worse?

Curr Biol

14

R233

R235

34. KembelSWCowanPDHelmusMRCornwellWKMorlonH

2010

Picante: R tools for integrating phylogenies and

ecology.

Bioinformatics

26

1463

1464

35. R Development Core Team

2010

R: A language and environment for statistical

computing.

Version 2.11.1

Vienna

R Foundation for Statistical Computing

##### Štítky

Genetika Reprodukční medicínaČlánek vyšel v časopise

### PLOS Genetics

2011 Číslo 6

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…

- Statistical Inference on the Mechanisms of Genome Evolution
- Exome Sequencing Identifies Mutations in High Myopia
- Finished Genome of the Fungal Wheat Pathogen Reveals Dispensome Structure, Chromosome Plasticity, and Stealth Pathogenesis
- Integrating 5-Hydroxymethylcytosine into the Epigenomic Landscape of Human Embryonic Stem Cells

Zvyšte si kvalifikaci online z pohodlí domova

##### Kurz originály vs. generika

nový kurzVšechny kurzy