skip to main content


Search for: All records

Creators/Authors contains: "Liu, Jun S."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available January 1, 2025
  2. Free, publicly-accessible full text available January 1, 2025
  3. Free, publicly-accessible full text available December 8, 2024
  4. Free, publicly-accessible full text available January 1, 2025
  5. Free, publicly-accessible full text available October 6, 2024
  6. Abstract The multiple-try Metropolis method is an interesting extension of the classical Metropolis–Hastings algorithm. However, theoretical understanding about its usefulness and convergence behavior is still lacking. We here derive the exact convergence rate for the multiple-try Metropolis Independent sampler (MTM-IS) via an explicit eigen analysis. As a by-product, we prove that an naive application of the MTM-IS is less efficient than using the simpler approach of “thinned” independent Metropolis–Hastings method at the same computational cost. We further explore more variants and find it possible to design more efficient algorithms by applying MTM to part of the target distribution or creating correlated multiple trials. 
    more » « less
    Free, publicly-accessible full text available August 1, 2024
  7. Nielsen, Rasmus (Ed.)
    Abstract

    An important goal of evolutionary genomics is to identify genomic regions whose substitution rates differ among lineages. For example, genomic regions experiencing accelerated molecular evolution in some lineages may provide insight into links between genotype and phenotype. Several comparative genomics methods have been developed to identify genomic accelerations between species, including a Bayesian method called PhyloAcc, which models shifts in substitution rate in multiple target lineages on a phylogeny. However, few methods consider the possibility of discordance between the trees of individual loci and the species tree due to incomplete lineage sorting, which might cause false positives. Here, we present PhyloAcc-GT, which extends PhyloAcc by modeling gene tree heterogeneity. Given a species tree, we adopt the multispecies coalescent model as the prior distribution of gene trees, use Markov chain Monte Carlo (MCMC) for inference, and design novel MCMC moves to sample gene trees efficiently. Through extensive simulations, we show that PhyloAcc-GT outperforms PhyloAcc and other methods in identifying target lineage-specific accelerations and detecting complex patterns of rate shifts, and is robust to specification of population size parameters. PhyloAcc-GT is usually more conservative than PhyloAcc in calling convergent rate shifts because it identifies more accelerations on ancestral than on terminal branches. We apply PhyloAcc-GT to two examples of convergent evolution: flightlessness in ratites and marine mammal adaptations, and show that PhyloAcc-GT is a robust tool to identify shifts in substitution rate associated with specific target lineages while accounting for incomplete lineage sorting.

     
    more » « less
    Free, publicly-accessible full text available September 1, 2024
  8. Free, publicly-accessible full text available July 1, 2024
  9. Free, publicly-accessible full text available July 3, 2024
  10. Free, publicly-accessible full text available July 3, 2024