Jove
Visualize
Contact Us

Related Experiment Videos

A powerful and robust method for mapping quantitative trait loci in general pedigrees.

G Diao1, D Y Lin

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7420, USA.

American Journal of Human Genetics
|May 27, 2005
PubMed
Summary

This study introduces a new variance-components model for quantitative trait loci mapping that handles non-normal data and outliers effectively. The novel method improves power and maintains accuracy, outperforming existing approaches when trait distributions deviate from normality.

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

Checking the Cox Proportional Hazards Model with Interval-Censored Data.

Journal of the American Statistical Association·2025
Same author

Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State.

Journal of the American Statistical Association·2025
Same author

Multiancestry Genome-Wide Association Study of Early Childhood Caries.

Journal of dental research·2024
Same author

Maximum likelihood estimation for semiparametric regression models with interval-censored multistate data.

Biometrika·2024
Same author

Multi-ancestry Genome-Wide Association Study of Early Childhood Caries.

medRxiv : the preprint server for health sciences·2024
Same author

Marginal proportional hazards models for multivariate interval-censored data.

Biometrika·2023
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

Area of Science:

  • Genetics
  • Statistical genetics
  • Human genetics

Background:

  • The variance-components model is standard for quantitative trait loci (QTL) mapping in human pedigrees.
  • This model assumes normally distributed trait values, and violations can negatively impact type I error rates and statistical power.
  • Common data transformations are often ineffective for non-normal data and outlying values.

Purpose of the Study:

  • To develop a novel extension of the variance-components model that accommodates unspecified transformation functions.
  • To enable accurate QTL mapping without strict normality assumptions.
  • To improve robustness against outlying trait values in genetic analyses.

Main Methods:

  • Proposed a novel variance-components model with an unspecified transformation function.

Related Experiment Videos

  • Developed efficient likelihood-based procedures for estimating variance components and testing genetic linkage.
  • Utilized simulation studies to evaluate the method's performance under various conditions.
  • Applied the method to a genomewide scan for monoamine oxidase B in the Collaborative Study on the Genetics of Alcoholism.

    • Main Results:

    • The new method demonstrated comparable power to existing methods under normality.
    • It provided accurate type I error control and substantially increased power when normality assumptions were violated.
    • Analysis of the monoamine oxidase B data showed robust results, unaffected by outlying trait values, unlike the existing method.

    Conclusions:

    • The proposed unspecified transformation variance-components model offers a robust and powerful alternative for QTL mapping.
    • It effectively addresses issues of non-normality and outliers, leading to more reliable genetic linkage results.
    • The freely available software facilitates its application in human genetic studies.