Recombination is a key evolutionary process that shapes the architecture of genomes and the genetic structure of populations. Although many statistical methods are available for the detection of recombination from DNA sequences, their absolute and relative performance is still unknown. Here we evaluated the performance of 14 different recombination detection algorithms. We used the coalescent with recombination to simulate DNA sequences with different levels of recombination, genetic diversity, and rate variation among sites. Recombination detection methods were applied to these data sets, and whether they detected or not recombination was recorded. Different recombination methods showed distinct performance depending on the amount of recombination, genetic diversity, and rate variation among sites. The model of nucleotide substitution under which the data were generated did not seem to have a significant effect. Most methods increase power with more sequence divergence. In general, recombination detection methods seem to capture the presence of recombination, but they are not very powerful. Methods that use substitution patterns or incompatibility among sites were more powerful than methods based on phylogenetic incongruence. Most methods do not seem to infer more false positives than expected by chance. Especially depending on the amount of diversity in the data, different methods could be used to attain maximum power while minimizing false positives. Results shown here will provide some guidance in the selection of the most appropriate method͞s for the analysis of the particular data at hand. R ecombination, defined here as the exchange of genetic information between two nucleotide sequences, is an important process that influences biological evolution at many different levels. Recombination explains a considerable amount of genetic diversity in natural populations and, in general, genes located in regions of the genome with low levels of recombination have low levels of polymorphism. Recombination reshuffles existing variation and even creates new variants at the amino acid level. Indeed, recombination shapes the genetic structure of natural populations (1, 2) and the action of natural selection (3). Characterization of the role of recombination across genomes is of major interest. The study of recombination events will allow us to better understand the dynamics of genomes (4, 5). Recombination breaks down linkage disequilibrium and, consequently, the characterization of recombination is essential for gene mapping, quantitative trait loci, and association studies (6). In addition, recombination has a significant impact on the evolution of several human pathogens (7-9) and consequently on their clinical treatment and prevention. Moreover, many applications in biology today are based on the estimation of phylogenetic trees. One main assumption of most phylogenetic methods is that there is only one phylogeny underlying the evolution of the sequences under study. Recombination violates this assumption by generating mosaic genes, where different regions have different phylogenetic histories. By ignoring the presence of recombination, phylogenetic analysis may be severely compromised (10-13). For all these reasons, the accurate detection of recombination from DNA sequences becomes very relevant, and indeed a number of methods have been developed for that purpose (D. L. Robertson, http:͞͞grinch.zoo.ox.ac.uk͞RAPlinks.html). Surprisingly, only a few studies have attempted to examine the relative performance of these methods (14-17). Although useful, these studies have been limited in the number of methods compared and the set of conditions evaluated. In practice, researchers are unable to make an objective selection of the most suitable method to detect recombination in their data. Here we perform a comprehensive analysis of 14 different methods for detecting recombination to determine relative performance and associated conditions of performance. We simulated DNA sequences with different rates of recombination, diversity, and rate heterogeneity to investigate the statistical power and the rate of false positives of the 14 different recombination detection algorithms. A glossary of terms is described in Table 1 . To study the statistical power (1-rate of Type II error, or the probability of rejecting the null hypothesis-no recombination-when it is false) and the rate of false positives (Type I error, or the probability of rejecting the null hypothesis when it is true) of the methods evaluated, two sets of simulations were carried out. In the first set (Simulations I, power analysis; Table 2 ), an increasing recombination rate was simulated for different levels of variation. In the second set (Simulations II, false positives; Table 3 ), increasing rate variation among sites was simulated for different levels of variation and without recombination. This design allows us to examine the confounding effect of rate variation with recombination (10). For each set of conditions, 100 replicates were simulated. The range of parameter values used in the simulations is commonly observed in real data sets. Software to perform these simulations is available on request. The general simulation strategy was: (i) Simulate recombinant genealogies by using the coalescent with recombination. (ii) Evolve nucleotide sequences on the simulated genealogy to obtain a sequence alignment. (iii) Apply 14 different methods to the simulated data and record how many times, of 100 replicates, a method infers the presence of recombination. The Coalescent with Recombination. Multiple genealogies for samples of n ϭ 10 sequences were simulated under the coalescent with recombination (18) (19) (20) (21) (22) (23) (24) (25) (26) . In the recombination model implemented here, there are n sequences with l sites, and the population consists of N diploid individuals. A continuous time approximation is used, and the time is scaled in units of 2N generations. The recombination rate () is defined as ϭ 4Nrl, where r is the rate of recombination per site per generation. The coalescent is built backwards in time. It is constructed by waiting for recombination or coalescent events until all ancestral sites in the n sequences have found a most recent common ancestor (not necessarily the same ancestor for all sites). The waiting times to a coalescent event are exponentially distributed with mean k(k Ϫ 1)͞2 (k is the number of sequences at a given
Although many of these methods can be effective, their performance is highly dependent on their parameter values and the rates of evolutionary events that formed the input sequences REF .
Evaluation of methods for detecting recombination from DNA sequences: Computer simulations
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Medicine Biology