Bayesian random local clocks, or one rate to rule them all.
BACKGROUND
Relaxed molecular clock models allow divergence time dating and "relaxed phylogenetic" inference, in which a time tree is estimated in the face of unequal rates across lineages. We present a new method for relaxing the assumption of a strict molecular clock using Markov chain Monte Carlo to implement Bayesian modeling averaging over random local molecular clocks. The new method approaches the problem of rate variation among lineages by proposing a series of local molecular clocks, each extending over a subregion of the full phylogeny. Each branch in a phylogeny (subtending a clade) is a possible location for a change of rate from one local clock to a new one. Thus, including both the global molecular clock and the unconstrained model results, there are a total of 2(2n-2) possible rate models available for averaging with 1, 2, ..., 2n - 2 different rate categories.


RESULTS
We propose an efficient method to sample this model space while simultaneously estimating the phylogeny. The new method conveniently allows a direct test of the strict molecular clock, in which one rate rules them all, against a large array of alternative local molecular clock models. We illustrate the method's utility on three example data sets involving mammal, primate and influenza evolution. Finally, we explore methods to visualize the complex posterior distribution that results from inference under such models.


CONCLUSIONS
The examples suggest that large sequence datasets may only require a small number of local molecular clocks to reconcile their branch lengths with a time scale. All of the analyses described here are implemented in the open access software package BEAST 1.5.4 (http://beast-mcmc.googlecode.com/).
Drummond
Alexei J
AJ
Allan Wilson Centre for Molecular Ecology and Evolution, University of Auckland, Private Bag 92019, Auckland, New Zealand. alexei@cs.auckland.ac.nz
Suchard
Marc A
MA
D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D004272:DNA, Mitochondrial / Q000235:genetics; D019143:Evolution, Molecular; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000235:genetics; D020407:Internet; D008322:Mammals / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D010802:Phylogeny; D012984:Software; D013997:Time Factors
D004272:DNA, Mitochondrial; D019267:Hemagglutinin Glycoproteins, Influenza Virus; C483468:hemagglutinin, human influenza A virus
Mol Biol Evol. 2000 Jul;17(7):1081-90
10889221
J Mol Evol. 1981;17(6):368-76
7288891
Syst Biol. 2004 Jun;53(3):485-95
15503675
Biometrics. 2005 Sep;61(3):665-73
16135017
Syst Biol. 2006 Apr;55(2):195-207
16522570
J Mol Evol. 1984;20(1):86-93
6429346
Mol Biol Evol. 2001 Mar;18(3):352-61
11230536
Trends Ecol Evol. 1996 Sep;11(9):367-72
21237881
Mol Biol Evol. 2002 Jan;19(1):101-9
11752195
J Mol Evol. 1985;22(2):160-74
3934395
Nat Rev Genet. 2003 Mar;4(3):216-24
12610526
Mol Biol Evol. 2011 Jan;28(1):523-32
20801907
PLoS Biol. 2006 May;4(5):e88
16683862
Syst Biol. 2007 Jun;56(3):453-66
17558967
Mol Biol Evol. 2007 Aug;24(8):1586-91
17483113
Bioinformatics. 2001 Aug;17(8):754-5
11524383
J Mol Evol. 2003;57 Suppl 1:S201-13
15008417
Genetics. 2010 May;185(1):313-26
20176979
Science. 1967 Dec 1;158(3805):1200-3
4964406
BMC Evol Biol. 2007 Nov 08;7:214
17996036
Genetics. 2000 Apr;154(4):1879-92
10747076
J Mol Evol. 1994 Sep;39(3):306-14
7932792
Syst Biol. 2002 Oct;51(5):689-702
12396584
Mol Biol Evol. 2001 Jun;18(6):1001-13
11371589
Mol Biol Evol. 2001 Jul;18(7):1365-71
11420374
Mol Biol Evol. 1998 Dec;15(12):1647-57
9866200
Science. 2009 Jan 23;323(5913):479-83
19164742
J Mol Evol. 1982;18(4):225-39
6284948
Genetics. 2002 Jul;161(3):1307-20
12136032
BMC Genet. 2008 Oct 31;9:68
18976476
Mol Biol Evol. 1989 Nov;6(6):636-47
2488476
Mol Biol Evol. 1988 Nov;5(6):626-44
3146681
Syst Biol. 2005 Jun;54(3):401-18
16012107
Syst Biol. 2003 Feb;52(1):48-54
12554439