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Related Experiment Video

Updated: Jun 24, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Published on: February 3, 2023

Non-iterative variance component estimation in QTL analysis.

L Rönnegård1, R Al-Sarraj, D von Rosen

  • 1Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden. lrn@du.se

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie
|March 27, 2009
PubMed
Summary

A new non-iterative method for quantitative trait loci (QTL) variance estimation was developed. This approach provides accurate QTL variance estimates comparable to the standard REML method, aiding genetic analysis.

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Published on: January 16, 2019

Area of Science:

  • Quantitative genetics
  • Statistical genomics
  • Animal breeding

Background:

  • Variance component quantitative trait loci (QTL) analysis utilizes mixed models to identify chromosomal locations of QTL.
  • Estimating QTL variance is crucial, with restricted maximum likelihood (REML) being the standard iterative method.

Purpose of the Study:

  • To introduce a novel non-iterative variance component estimation method for QTL analysis.
  • To compare the performance of this new method against the established REML approach.

Main Methods:

  • Developed a non-iterative variance component estimation method based on Henderson's method 3, relaxing unbiasedness.
  • Compared two estimators derived from different sum of squares partitions within Henderson's method 3.
  • Applied and compared the novel method with REML using European wild boar x domestic pig intercross data.

Main Results:

  • The non-iterative estimators yielded QTL variance estimates closely matching those from REML.
  • The method was applied to a meat quality trait on chromosome 6 in a pig intercross.
  • Approximations of the likelihood ratio curve were successfully computed from the non-iterative estimates.

Conclusions:

  • The novel non-iterative method offers a viable and efficient alternative for QTL variance estimation.
  • This method provides accurate estimates and facilitates likelihood-based analyses in QTL mapping studies.