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Influence analysis in quantitative trait loci detection.

Xiaoling Dou1, Satoshi Kuriki, Akiteru Maeno

  • 1The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan.

Biometrical Journal. Biometrische Zeitschrift
|April 18, 2014
PubMed
Summary

This study introduces new methods to identify individuals impacting genetic analysis scores. These techniques improve the accuracy of quantitative trait locus detection by analyzing the influence of specific individuals on results.

Keywords:
Influence score vectorProfile likelihoodROC analysisShape of LOD score curveStandardized empirical influence function

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Quantitative trait locus (QTL) detection is crucial for understanding genetic contributions to complex traits.
  • Identifying influential individuals is essential for robust genetic analysis and accurate LOD score interpretation.
  • Existing methods may lack sensitivity in detecting subtle individual influences on QTL analysis.

Purpose of the Study:

  • To develop systematic methods for detecting influential individuals affecting the log odds (LOD) score curve in genetic analyses.
  • To introduce influence functions for profile likelihoods into standard QTL detection methods.
  • To assess the significance and performance of these novel influence analysis methods.

Main Methods:

  • Derivation of general formulas for influence functions of profile likelihoods.
  • Integration of influence functions into interval mapping and single marker analysis.
  • Development of methods for analyzing the shape of LOD score curves and simulation-based significance assessment.
  • Receiver operating characteristic (ROC) analysis for performance evaluation.

Main Results:

  • The proposed methods effectively detect influential individuals impacting LOD score curves.
  • Influence analysis on both specific LOD scores and the overall curve shape was achieved.
  • Simulation studies confirmed the utility of the methods.
  • Real data analysis on an F2 mouse cross demonstrated the practical application.
  • Proposed methods outperformed existing diagnostics in ROC analysis.

Conclusions:

  • The developed systematic methods provide a powerful tool for identifying influential individuals in genetic studies.
  • These methods enhance the reliability and accuracy of QTL detection.
  • The approach offers improved diagnostic performance compared to existing techniques for influence analysis in genetic data.