Estimating Divergence Time and Ancestral Effective Population Size of Bornean and Sumatran Orangutan Subspecies Using a Coalescent Hidden Markov Model
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{"title"=>"Estimating divergence time and ancestral effective population size of Bornean and Sumatran orangutan subspecies using a coalescent hidden Markov model", "type"=>"journal", "authors"=>[{"first_name"=>"Thomas", "last_name"=>"Mailund", "scopus_author_id"=>"14619483800"}, {"first_name"=>"Julien Y.", "last_name"=>"Dutheil", "scopus_author_id"=>"24504375400"}, {"first_name"=>"Asger", "last_name"=>"Hobolth", "scopus_author_id"=>"6602567701"}, {"first_name"=>"Gerton", "last_name"=>"Lunter", "scopus_author_id"=>"8882130500"}, {"first_name"=>"Mikkel H.", "last_name"=>"Schierup", "scopus_author_id"=>"6603704923"}], "year"=>2011, "source"=>"PLoS Genetics", "identifiers"=>{"issn"=>"15537390", "scopus"=>"2-s2.0-79953744766", "sgr"=>"79953744766", "pui"=>"361565182", "isbn"=>"1553-7404 (Electronic)\\n1553-7390 (Linking)", "pmid"=>"21408205", "doi"=>"10.1371/journal.pgen.1001319"}, "id"=>"282a2a7d-2213-3847-8818-edd5e7b720f2", "abstract"=>"Due to genetic variation in the ancestor of two populations or two species, the divergence time for DNA sequences from two populations is variable along the genome. Within genomic segments all bases will share the same divergence-because they share a most recent common ancestor-when no recombination event has occurred to split them apart. The size of these segments of constant divergence depends on the recombination rate, but also on the speciation time, the effective population size of the ancestral population, as well as demographic effects and selection. Thus, inference of these parameters may be possible if we can decode the divergence times along a genomic alignment. Here, we present a new hidden Markov model that infers the changing divergence (coalescence) times along the genome alignment using a coalescent framework, in order to estimate the speciation time, the recombination rate, and the ancestral effective population size. The model is efficient enough to allow inference on whole-genome data sets. We first investigate the power and consistency of the model with coalescent simulations and then apply it to the whole-genome sequences of the two orangutan sub-species, Bornean (P. p. pygmaeus) and Sumatran (P. p. abelii) orangutans from the Orangutan Genome Project. We estimate the speciation time between the two sub-species to be thousand years ago and the effective population size of the ancestral orangutan species to be , consistent with recent results based on smaller data sets. We also report a negative correlation between chromosome size and ancestral effective population size, which we interpret as a signature of recombination increasing the efficacy of selection.", "link"=>"http://www.mendeley.com/research/estimating-divergence-time-ancestral-effective-population-size-bornean-sumatran-orangutan-subspecies", "reader_count"=>164, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>15, "Librarian"=>1, "Researcher"=>38, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>59, "Student > Postgraduate"=>6, "Student > Master"=>13, "Other"=>4, "Student > Bachelor"=>8, "Lecturer"=>5, "Lecturer > Senior Lecturer"=>2, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>15, "Librarian"=>1, "Researcher"=>38, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>59, "Student > Postgraduate"=>6, "Student > Master"=>13, "Other"=>4, "Student > Bachelor"=>8, "Lecturer"=>5, "Lecturer > Senior Lecturer"=>2, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>14, "Mathematics"=>3, "Agricultural and Biological Sciences"=>128, "Arts and Humanities"=>2, "Social Sciences"=>4, "Computer Science"=>6, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Social Sciences"=>{"Social Sciences"=>4}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>128}, "Computer Science"=>{"Computer Science"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>14}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>2}}, "reader_count_by_country"=>{"Saudi Arabia"=>1, "Austria"=>2, "Netherlands"=>1, "Sweden"=>2, "United States"=>10, "Brazil"=>2, "Denmark"=>2, "United Kingdom"=>3, "Italy"=>1, "France"=>1, "Germany"=>3, "Spain"=>3}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/793757"], "description"=>"<p>The box plot shows the distribution of the estimated recombination rate for each chromosome. The dashed line shows the genome-wide average.</p>", "links"=>[], "tags"=>["recombination", "estimates"], "article_id"=>464129, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g011", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_recombination_rate_estimates_for_each_chromosome_/464129", "title"=>"Distribution of recombination rate estimates for each chromosome.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:08:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/793890"], "description"=>"<p>A) There is no correlation between the estimated sub-speciation time and chromosome size. B) A negative correlation between chromosome size and estimated recombination rate (). C) A negative correlation between chromosome size and inferred effective population size (). The X chromosome was removed from the regression of effective population size.</p>", "links"=>[], "tags"=>["inferred", "parameters", "chromosome"], "article_id"=>464258, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g013", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_correlation_of_inferred_parameters_with_chromosome_size_/464258", "title"=>"The correlation of inferred parameters with chromosome size.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:10:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/794016"], "description"=>"<p>Genome-wide estimates of the key parameters, with and without outlier estimates removed. The ancestral effective population size is estimated separately for the autosomal chromosomes and the X chromosome since the X chromosome by coalescence theory is expected to have an effective population size of of the autosomes.</p>", "links"=>[], "tags"=>["parameter"], "article_id"=>464385, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genome_wide_parameter_estimates_/464385", "title"=>"Genome-wide parameter estimates.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-03 01:13:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/793704"], "description"=>"<p>The box plot shows the distribution of the estimated ancestral effective population on each chromosome. The dashed line shows the genome-wide average. In general, the estimates are reasonably consistent between the chromosomes, with one exception being chromosome 21. Chromosome X is within the expected range of of the autosomal chromosomes.</p>", "links"=>[], "tags"=>["estimates"], "article_id"=>464075, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g010", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_effective_population_size_estimates_for_each_chromosome_/464075", "title"=>"Distribution of effective population size estimates for each chromosome.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:07:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/793326"], "description"=>"<p>The figure shows the beginning states in the two-sequence CTMC and how their probabilities evolve over time. The asymmetry between which original sequence is linked versus unlinked, when only one sequence is linked (the second plot from the top) is caused by the differences in effective population size within the single-sequence CTMC system used for determining the initial distribution of the two-sequence distribution. The initial probability is taken from the two populations in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1001319#pgen-1001319-g003\" target=\"_blank\">Figure 3</a> at time 10 (the right edge of that figure) and the rates in the two-sequence system correspond to those for population 1 in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1001319#pgen-1001319-g003\" target=\"_blank\">Figure 3</a>, i.e. a coalescence rate of 1 and a recombination rate of .</p>", "links"=>[], "tags"=>["states", "two-sequence"], "article_id"=>463693, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g005", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolution_of_states_in_the_two_sequence_CTMC_/463693", "title"=>"Evolution of states in the two-sequence CTMC.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:01:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/793975"], "description"=>"<p>Left: Parameters of the model; we use the ancestral population as the reference population. Right: Rate matrices for the single sequence systems and (ancestral) two-sequence system.</p>", "links"=>[], "tags"=>["computational biology/comparative sequence analysis", "computational biology/evolutionary modeling", "computational biology/genomics", "computational biology/population genetics", "evolutionary biology/bioinformatics", "evolutionary biology/genomics", "molecular biology/bioinformatics", "molecular biology/molecular evolution"], "article_id"=>464339, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g014", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Isolation_model_/464339", "title"=>"Isolation model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:12:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/793392"], "description"=>"<p>The figure shows how the probability of being in one of the four classes of states evolve over time. Initially, the system is in with probability 1, but this probability drops exponentially. With a relatively small probability the system will go through a state in or before ending up in but mainly states move directly to states. The rate parameters used are the same as those in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1001319#pgen-1001319-g005\" target=\"_blank\">Figure 5</a>.</p>", "links"=>[], "tags"=>["two-sequence"], "article_id"=>463765, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolution_of_the_two_sequence_CTMC_/463765", "title"=>"Evolution of the two-sequence CTMC.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:02:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/793278"], "description"=>"<p>For the two species system, we have one or two left and one or two right nucleotides. One when the left or right nucleotides have found their MRCA (open circles) and two otherwise (filled circles). Left nucleotides can be linked with right nucleotides or not.</p>", "links"=>[], "tags"=>["computational biology/comparative sequence analysis", "computational biology/evolutionary modeling", "computational biology/genomics", "computational biology/population genetics", "evolutionary biology/bioinformatics", "evolutionary biology/genomics", "molecular biology/bioinformatics", "molecular biology/molecular evolution"], "article_id"=>463638, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g004", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_States_for_the_two_species_system_/463638", "title"=>"States for the two species system.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:00:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/793541"], "description"=>"<p>The boxplots show the estimated parameters (divergence time, ancestral effective population size, and recombination rate) for 100 simulated data sets. The true value is showed as the blue dashed line. The number of states in the HMM, i.e. the number of coalescence time intervals used for the estimation takes the values 5, 10 and 15. There is a clear bias in the estimates where we tend to underestimate the divergence time and overestimate the effective population size. This bias is caused by the discretisation of continuous coalescence times into fixed intervals, and the bias is reduced as the number of states (i.e. intervals) increases. The recombination rate is under-estimated, which is a consequence of the Markov assumption.</p>", "links"=>[], "tags"=>["computational biology/comparative sequence analysis", "computational biology/evolutionary modeling", "computational biology/genomics", "computational biology/population genetics", "evolutionary biology/bioinformatics", "evolutionary biology/genomics", "molecular biology/bioinformatics", "molecular biology/molecular evolution"], "article_id"=>463903, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g008", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimation_accuracy_as_a_function_of_the_number_of_hidden_states_/463903", "title"=>"Estimation accuracy as a function of the number of hidden states.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:05:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/793471"], "description"=>"<p>The hidden Markov model transition probabilities are calculated by considering the probabilities, in the two nucleotide CTMC system, that either both left and right nucleotide finds a most recent common ancestor in the same time interval (left) or that the left nucleotide finds a most recent common ancestor in one given interval, , and the right in another, (right).</p>", "links"=>[], "tags"=>["probabilities", "calculated", "ctmc"], "article_id"=>463840, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g007", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transition_probabilities_calculated_from_the_CTMC_system_/463840", "title"=>"Transition probabilities calculated from the CTMC system.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:04:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/793224"], "description"=>"<p>The figure shows the probability of the two neighboring nucleotides being linked in the single-sequence CTMC and how this probability evolves over time. The probability for being unlinked is, of course, one minus the probability of being linked, since the CTMC has only two states. The two different lines correspond to two different coalescence rates. For population 1, the coalescence rate is set to 1 (so the x-axis is in units of generations for this species), while for population 2 the coalescence rate is half that, corresponding to population 2 having twice the effective population size of population 1. The recombination rate is set to . Assuming of 20,000 for population 1 and 40,000 for population 2, and a generation time of 20 years, this corresponds to 1 cM/Mbp and 1 unit on the x-axis corresponds to 400,000 years. The separation time we infer for the orangutan sub-species is around on the x-axis, where the system is still far from equilibrium.</p>", "links"=>[], "tags"=>["probabilities", "two-nucleotide", "coalescent"], "article_id"=>463591, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_State_probabilities_for_the_single_sequence_two_nucleotide_coalescent_process_as_a_function_of_time_/463591", "title"=>"State probabilities for the single sequence two-nucleotide coalescent process as a function of time.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 00:59:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/793827"], "description"=>"<p>There is a significant positive correlation between recombination rate and (equilibrium) GC content ().</p>", "links"=>[], "tags"=>["gc", "inferred", "recombination"], "article_id"=>464194, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g012", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_correlation_between_GC_content_and_inferred_recombination_rate_/464194", "title"=>"The correlation between GC content and inferred recombination rate.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:09:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/793179"], "description"=>"<p>For a single species we have two nucleotides, one left and one right, and these can either be linked or unlinked.</p>", "links"=>[], "tags"=>["computational biology/comparative sequence analysis", "computational biology/evolutionary modeling", "computational biology/genomics", "computational biology/population genetics", "evolutionary biology/bioinformatics", "evolutionary biology/genomics", "molecular biology/bioinformatics", "molecular biology/molecular evolution"], "article_id"=>463540, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_States_for_the_single_species_system_/463540", "title"=>"States for the single species system.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 00:59:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/793623"], "description"=>"<p>The box plot shows the distribution of the estimated sub-speciation time on each chromosome. The dashed line shows the genome-wide average. In general, the estimates are reasonably consistent between the chromosomes, although chromosome 21 has a slightly smaller value.</p>", "links"=>[], "tags"=>["sub-speciation", "estimates"], "article_id"=>463991, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g009", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_sub_speciation_time_estimates_for_each_chromosome_/463991", "title"=>"Distribution of sub-speciation time estimates for each chromosome.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 01:06:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/793115"], "description"=>"<p>The figure illustrates the ancestry of two genomic sequences (in red and blue) from two different populations. Tracing their ancestry back in time, the first event we see is a recombination (at time and sequence position ) within the “blue population”. This is followed by the population split (at time ), and thus the “red genome” enters the ancestral population as a contiguous segment, while the “blue genome” enters the ancestral population in two fragments. The process in the ancestral population now under goes coalescence events (at times , , and ) and a recombination event (at time and sequence position ). A consequence of the coalescence process with recombination is that the coalescence times, and thus the sequence divergence, changes along the genomic alignment at the recombination break points (illustrated on the right).</p>", "links"=>[], "tags"=>["ancestry", "genomic"], "article_id"=>463480, "categories"=>["Evolutionary Biology", "Medicine", "Infectious Diseases", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_ancestry_of_two_genomic_sequences_/463480", "title"=>"The ancestry of two genomic sequences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-03 00:58:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/397120"], "description"=>"<div><p>Due to genetic variation in the ancestor of two populations or two species, the divergence time for DNA sequences from two populations is variable along the genome. Within genomic segments all bases will share the same divergence—because they share a most recent common ancestor—when no recombination event has occurred to split them apart. The size of these segments of constant divergence depends on the recombination rate, but also on the speciation time, the effective population size of the ancestral population, as well as demographic effects and selection. Thus, inference of these parameters may be possible if we can decode the divergence times along a genomic alignment. Here, we present a new hidden Markov model that infers the changing divergence (coalescence) times along the genome alignment using a coalescent framework, in order to estimate the speciation time, the recombination rate, and the ancestral effective population size. The model is efficient enough to allow inference on whole-genome data sets. We first investigate the power and consistency of the model with coalescent simulations and then apply it to the whole-genome sequences of the two orangutan sub-species, Bornean (<em>P. p. pygmaeus</em>) and Sumatran (<em>P. p. abelii</em>) orangutans from the Orangutan Genome Project. We estimate the speciation time between the two sub-species to be thousand years ago and the effective population size of the ancestral orangutan species to be , consistent with recent results based on smaller data sets. We also report a negative correlation between chromosome size and ancestral effective population size, which we interpret as a signature of recombination increasing the efficacy of selection.</p></div>", "links"=>[], "tags"=>["estimating", "divergence", "ancestral", "bornean", "sumatran", "orangutan", "subspecies", "coalescent", "markov"], "article_id"=>138428, "categories"=>["Evolutionary Biology", "Medicine", "Cancer", "Molecular Biology"], "users"=>["Thomas Mailund", "Julien Y. Dutheil", "Asger Hobolth", "Gerton Lunter", "Mikkel H. Schierup"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1001319", "stats"=>{"downloads"=>3, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Estimating_Divergence_Time_and_Ancestral_Effective_Population_Size_of_Bornean_and_Sumatran_Orangutan_Subspecies_Using_a_Coalescent_Hidden_Markov_Model/138428", "title"=>"Estimating Divergence Time and Ancestral Effective Population Size of Bornean and Sumatran Orangutan Subspecies Using a Coalescent Hidden Markov Model", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-03 02:20:28"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2014", "month"=>"12"}
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  • {"unique-ip"=>"13", "full-text"=>"15", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"11"}
  • {"unique-ip"=>"24", "full-text"=>"18", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2015", "month"=>"12"}
  • {"unique-ip"=>"16", "full-text"=>"10", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"1"}
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  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"3"}
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  • {"unique-ip"=>"8", "full-text"=>"10", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"5"}
  • {"unique-ip"=>"10", "full-text"=>"8", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"6"}
  • {"unique-ip"=>"10", "full-text"=>"4", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"7"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"8"}
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  • {"unique-ip"=>"5", "full-text"=>"3", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"10"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"1"}
  • {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"8"}
  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"10"}
  • {"unique-ip"=>"7", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"12"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"9", "full-text"=>"12", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"5", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"7", "full-text"=>"5", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"9", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"23", "full-text"=>"16", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"6", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"11", "full-text"=>"13", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"15", "full-text"=>"10", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
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Relative Metric

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