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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Generalized linear mixed model for segregation distortion analysis.

Haimao Zhan¹, Shizhong Xu

¹Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.

|November 15, 2011

Summary

We developed a new statistical model to map segregation distortion loci, which are genetic factors causing deviations from expected inheritance patterns. This method accurately identifies these loci and has broad applications in genetic research.

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Area of Science:

Genetics
Statistical Genetics
Genomics

Background:

Segregation distortion occurs when observed genotypic frequencies deviate from expected Mendelian ratios.
Viability selection on linked marker loci is a primary cause of segregation distortion.
Genome-wide marker data enables the mapping of these viability selection loci.

Purpose of the Study:

To develop a statistical model for jointly mapping all viability selection loci across the genome.
To address challenges in mapping numerous loci relative to sample size.

Main Methods:

A generalized linear mixed model (GLMM) was developed under a liability model.
A hierarchical GLMM was employed to manage a large number of loci compared to sample size.
The model was tested using an F(2) mouse family dataset.

Main Results:

A major segregation distortion locus was detected, explaining 75% of the underlying liability variance.
High power for detecting viability loci was confirmed through simulations.
A low false positive rate was demonstrated in replicated simulation experiments.

Conclusions:

The developed method effectively detects segregation distortion loci.
The approach is applicable to mapping quantitative trait loci (QTL) for disease traits in humans.
The method can also be used for QTL mapping in selected populations of plants and animals.