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Related Concept Videos

Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
X-linked Traits01:19

X-linked Traits

In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
X-linked Traits01:19

X-linked Traits

In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.

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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 model for interval mapping of quantitative trait loci.

Shizhong Xu1, Zhiqiu Hu

  • 1Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA. shizhong.xu@ucr.edu

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|February 25, 2010
PubMed
Summary

We developed a generalized linear model for quantitative trait loci (QTL) mapping in line crossing experiments. Two algorithms, expectation-maximization (EM) and generalized estimating equation (GEE), were compared for discrete traits, with GEE showing more robust convergence.

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Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Quantitative trait loci (QTL) mapping is crucial for understanding the genetic basis of complex traits.
  • Existing methods often focus on continuous traits, limiting discrete trait analysis.
  • Line crossing experiments are common in plant and animal breeding for genetic studies.

Purpose of the Study:

  • To develop and compare generalized linear models for QTL mapping of discrete traits.
  • To implement parameter estimation using expectation-maximization (EM) and generalized estimating equation (GEE) algorithms.
  • To provide a user-defined SAS procedure (PROC QTL) for applying these methods.

Main Methods:

  • Generalized linear model (GLM) framework for discrete traits (ordinal, binary, binomial, Poisson).
  • Parameter estimation via a mixture model-based EM algorithm and a GEE algorithm.
  • Heterogeneous residual variance model incorporated for improved accuracy.
  • Validation using simulated and real-world datasets.

Main Results:

  • Both EM and GEE algorithms provided comparable results in most analyses.
  • The EM algorithm demonstrated convergence issues when large QTL were within large marker intervals.
  • The GEE algorithm showed greater robustness in parameter estimation under challenging conditions.
  • The developed methods were successfully implemented and tested within a SAS environment.

Conclusions:

  • The developed generalized linear models offer a flexible approach for QTL mapping of discrete traits.
  • The GEE algorithm is recommended for its robustness, particularly in scenarios with large QTL and marker intervals.
  • The PROC QTL procedure facilitates the application of these advanced statistical methods in genetic research.