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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Generalized linear mixed models for mapping multiple quantitative trait loci.

X Che1, S Xu

  • 1Department of Statistics, University of California, Riverside, CA 92521, USA.

Heredity
|March 15, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces two methods, expectation and overdispersion, to handle missing genotypes in quantitative trait loci (QTL) mapping for discrete traits using generalized linear mixed models (GLMM). Both methods proved effective, with the overdispersion method showing slightly better performance in estimating QTL effects.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Area of Science:

  • Genetics
  • Quantitative Genetics
  • Statistical Genetics

Background:

  • Biological traits often exhibit discrete phenotypes but continuous genetic distributions due to multiple genes and environmental factors.
  • Quantitative trait loci (QTL) are genes influencing these complex traits.
  • Generalized linear mixed models (GLMM) can estimate QTL effects, even with more QTL than samples, by treating effects as random.

Purpose of the Study:

  • To evaluate methods for handling missing genotypes in multiple QTL mapping (MQM) for discrete traits within a GLMM framework.
  • To compare the efficiency of the expectation and overdispersion methods for QTL analysis.

Main Methods:

  • Examined two missing genotype-handling methods: the expectation method and the overdispersion method.
  • Utilized simulation studies to assess the performance of these methods under the GLMM framework.
  • Applied the GLMM with both methods to multiple QTL mapping for wheat female fertility.

Main Results:

  • Both the expectation and overdispersion methods demonstrated efficiency for MQM under GLMM.
  • The overdispersion method exhibited a slight advantage, yielding smaller mean-squared errors for estimated QTL effects compared to the expectation method.
  • Multiple QTL were successfully identified as contributors to the variation in the number of seeded spikelets in wheat.

Conclusions:

  • The expectation and overdispersion methods are viable for MQM in discrete traits using GLMM, even with missing genotype data.
  • The overdispersion method offers improved accuracy in estimating QTL effects.
  • This approach successfully identified multiple QTL influencing wheat female fertility, specifically the number of seeded spikelets.