‘At the crossroads towards polyploidy’: genomic divergence and extent of homoploid hybridization are drivers for the formation of the ox‐eye daisy polyploid complex ( Leucanthemum , Compositae‐Anthemideae)

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Abstract

<p class="first" id="d309718e99">Polyploidy plays a paramount role in phytodiversity, but the causes of this evolutionary pathway require further study. Here, we use phylogenetic methods to examine possible polyploidy-promoting factors by comparing diploid representatives of the comprehensive European polyploid complex Leucanthemum with members of its strictly diploid North African counterpart Rhodanthemum. We investigate genetic divergence and gene flow among all diploid lineages of both genera to evaluate the role of genomic differentiation and hybridization for polyploid speciation. To test whether hybridization in Leucanthemum has been triggered by the geological conditions during its diversification, we additionally generate a time-calibrated phylogeny of 46 species of the subtribe Leucantheminae. Leucanthemum shows a significantly higher genetic divergence and hybridization signal among diploid lineages compared with Rhodanthemum, in spite of a similar crown age and diversification pattern during the Quaternary. Our study demonstrates the importance of genetic differentiation among diploid progenitors and their concurrent affinity for natural hybridization for the formation of a polyploid complex. Furthermore, the role of climate-induced range overlaps on hybridization and polyploid speciation during the Quaternary is discussed. </p>

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Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

Guy Baele, Philippe Lemey, Trevor Bedford … (2012)

Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.

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Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation.

Matthew Stephens, Paul Scheet (2005)

Although many algorithms exist for estimating haplotypes from genotype data, none of them take full account of both the decay of linkage disequilibrium (LD) with distance and the order and spacing of genotyped markers. Here, we describe an algorithm that does take these factors into account, using a flexible model for the decay of LD with distance that can handle both "blocklike" and "nonblocklike" patterns of LD. We compare the accuracy of this approach with a range of other available algorithms in three ways: for reconstruction of randomly paired, molecularly determined male X chromosome haplotypes; for reconstruction of haplotypes obtained from trios in an autosomal region; and for estimation of missing genotypes in 50 autosomal genes that have been completely resequenced in 24 African Americans and 23 individuals of European descent. For the autosomal data sets, our new approach clearly outperforms the best available methods, whereas its accuracy in inferring the X chromosome haplotypes is only slightly superior. For estimation of missing genotypes, our method performed slightly better when the two subsamples were combined than when they were analyzed separately, which illustrates its robustness to population stratification. Our method is implemented in the software package PHASE (v2.1.1), available from the Stephens Lab Web site.