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

Human disease genes: patterns and predictions.

Nick G C Smith1, Adam Eyre-Walker

  • 1Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, 752 36 Uppsala, Sweden. nick.smith@ebc.uu.se

Gene
|October 31, 2003
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

Nucleotide diversity is a poor predictor of short-term adaptive potential.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Population Genetic Study of <i>vitellogenin</i> in Honey Bees (<i>Apis mellifera</i>) With European Ancestry Identifies Two Ancestral Genetic Backgrounds.

Ecology and evolution·2026
Same author

Levels of additive genetic variation vary substantially between species.

PLoS biology·2026
Same author

A Comparative Analysis of Long-Term Effective Population Sizes Across Eukaryotes.

Molecular ecology·2026
Same author

Estimates of the Mutation Rate per Year Can Explain Why the Molecular Clock Depends on Generation Time.

Molecular biology and evolution·2025
Same author

The Effect of the Presence and Absence of DNA Repair Genes on the Rate and Pattern of Mutation in Bacteria.

Genome biology and evolution·2024
Same journal

Face/off: phase-specific modeling of lineage plasticity using near-patient models in genitourinary cancers.

Gene·2026
Same journal

Hierarchical analysis of metabolic phenotype reveals distinct microbiota and circulatory transcriptome in metabolic dysfunction-associated steatotic liver disease.

Gene·2026
Same journal

Mutation T71R enhanced the structural stability and functional activity of wild type superoxide dismutase cloned from soil metagenome.

Gene·2026
Same journal

Reduced ATXN1 expression as an adverse prognostic indicator in Acute myeloid leukemia.

Gene·2026
Same journal

Constructing regulatory networks of Rubisco post-translational modifications: a novel avenue for engineering environment adaptive plants.

Gene·2026
Same journal

Traumatic brain injury enhances fracture healing by upregulating VNN1 to activate the Wnt/β-catenin signaling pathway.

Gene·2026
See all related articles

Disease genes evolve faster and are expressed in fewer tissues compared to other human genes. These evolutionary differences can help predict new disease-causing genes and understand human gene evolution.

Area of Science:

  • Evolutionary biology
  • Human genetics
  • Genomics

Background:

  • Understanding the evolutionary patterns of human genes is crucial for identifying disease-causing genes.
  • Distinguishing between disease and non-disease genes can provide insights into genetic disease mechanisms.

Purpose of the Study:

  • To compare evolutionary patterns of human disease genes versus non-disease genes.
  • To identify features that differentiate disease genes and assess their predictive power.
  • To explore factors influencing the evolution of disease genes.

Main Methods:

  • Comparative analysis of human and rodent gene alignments.
  • Calculation of nonsynonymous/synonymous substitution rate ratios (Ka/Ks).
  • Analysis of gene expression patterns and protein-coding sequence lengths.

Related Experiment Videos

  • Application of discriminant analysis for gene prediction.

    • Main Results:

    • Disease genes exhibit higher Ka/Ks ratios and synonymous substitution rates compared to non-disease genes.
    • Disease genes possess longer protein-coding sequences and are expressed in a narrower range of tissues.
    • Discriminant analysis indicates these features can predict human disease genes.
    • Protein function, inheritance mode, and disease-induced life expectancy reduction significantly affect Ka/Ks in disease genes.

    Conclusions:

    • Distinct evolutionary characteristics differentiate human disease genes from non-disease genes.
    • These evolutionary signatures hold potential for predicting novel disease genes.
    • Factors like protein function and disease severity influence the evolutionary trajectory of disease genes.