Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Distinguishing between selective sweeps and demography using DNA polymorphism data.

Jeffrey D Jensen1, Yuseob Kim, Vanessa Bauer DuMont

  • 1Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA.

Genetics
|May 25, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Non-Parametric Ancestry Adjustment for Polygenic Scores.

medRxiv : the preprint server for health sciences·2026
Same author

Comparing fine-scale mutation and recombination landscapes in rhesus macaque ( <i>Macaca mulatta</i> ) populations of Chinese and Indian descent inferred from both short- and long-read sequencing data.

bioRxiv : the preprint server for biology·2026
Same author

Inferring the demographic history of Chinese and Indian rhesus macaque ( <i>Macaca mulatta</i> ) populations from PacBio HiFi long-read sequencing data.

bioRxiv : the preprint server for biology·2026
Same author

Evolutionary genomics based on PacBio HiFi long-read sequencing data reveals the importance of structural variants in shaping population-specific differences between Chinese and Indian rhesus macaques ( <i>Macaca mulatta</i> ).

bioRxiv : the preprint server for biology·2026
Same author

Inferring the Demographic History of Coppery Titi Monkeys (Plecturocebus cupreus) From High-Quality, Whole-Genome, Population-Level Data.

American journal of primatology·2026
Same author

Inferring Patterns of Purifying, Positive, and Balancing Selection in the Coppery Titi Monkey (Plecturocebus cupreus) Utilizing a Well-Fit Evolutionary Baseline Model.

Genome biology and evolution·2026
Same journal

Coexistence of piRNA and KZFP defense systems: Evolutionary dynamics of layered defense against transposable elements.

Genetics·2026
Same journal

Creation and manipulation of bipartite expression transgenes in C. elegans using phiC31 recombinase.

Genetics·2026
Same journal

Inherited long telomeres induce a genome-wide transcriptional response in budding yeast.

Genetics·2026
Same journal

Adaptive Dynamics of Quantitative Traits in a Steadily Changing Environment.

Genetics·2026
Same journal

Functional Landscape of Zebrafish Gonadotropins and Receptors: A Comprehensive Genetic Analysis.

Genetics·2026
Same journal

Synergistic actions of Nup43 and Myosin VI drive actin cone assembly during Drosophila spermiogenesis.

Genetics·2026
See all related articles

The composite-likelihood-ratio (CLR) test for beneficial mutations is unreliable with population structure or bottlenecks, yielding high false positives. A new goodness-of-fit test accurately distinguishes true selection from demographic artifacts.

Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Statistical Genetics

Background:

  • Composite-likelihood methods are used to detect beneficial mutations.
  • The composite-likelihood-ratio (CLR) test was proposed by Kim and Stephan in 2002.
  • Detecting recent beneficial mutations is crucial for understanding adaptation.

Purpose of the Study:

  • To evaluate the robustness of the CLR test.
  • To identify factors causing false positives in the CLR test.
  • To develop a new method for distinguishing true selection from demographic effects.

Main Methods:

  • Simulations were used to test the CLR statistic.
  • Population structure and bottlenecks were simulated.
  • A novel goodness-of-fit test was developed and applied.

Related Experiment Videos

Main Results:

  • The CLR test showed a high false positive rate (up to 90%) under population structure and bottlenecks.
  • The proposed goodness-of-fit test effectively differentiated true selection from false positives.
  • The new method demonstrated high sensitivity in distinguishing rejection classes.

Conclusions:

  • The CLR test is not robust to demographic complexities.
  • Population structure and bottlenecks can mimic signals of selection.
  • The developed goodness-of-fit test provides a reliable way to interpret CLR test results in realistic population scenarios.