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Related Experiment Video

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

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Model-based linkage analysis of a quantitative trait.

Audrey H Schnell1, Xiangqing Sun

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA. ahs3@case.edu

Methods in Molecular Biology (Clifton, N.J.)
|February 7, 2012
PubMed
Summary
This summary is machine-generated.

This study demonstrates linkage analysis for quantitative traits using the S.A.G.E. software package. It details single and multipoint analyses to identify genetic markers associated with trait variation.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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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
  • Biostatistics
  • Quantitative Trait Analysis

Background:

  • Linkage analysis identifies genetic markers co-segregating with traits.
  • Historically used for binary traits, it's extended to quantitative traits for richer data.
  • Quantitative traits offer more information than binary traits in genetic studies.

Purpose of the Study:

  • To demonstrate the application of linkage analysis for quantitative traits.
  • To detail the use of the Statistical Analysis for Genetic Epidemiology (S.A.G.E.) software package.
  • To illustrate single and multipoint linkage analysis methods.

Main Methods:

  • Utilized the S.A.G.E. program package for data cleaning, genetic model testing, and linkage analysis.
  • Employed SEGREG to determine the optimal statistical model for the quantitative trait.
  • Performed single marker analysis using LODLINK and multipoint analysis using MLOD within S.A.G.E.

Main Results:

  • Detailed procedures for running LODLINK (single marker) and MLOD (multipoint) analyses are presented.
  • Demonstrated the integration of SEGREG model output with linkage analysis programs.
  • Provided a practical workflow for conducting quantitative trait linkage analysis.

Conclusions:

  • S.A.G.E. is a comprehensive tool for quantitative trait linkage analysis, including model selection and analysis.
  • The methodology facilitates the identification of genetic underpinnings for quantitative traits.
  • This approach supports the genetic basis investigation of complex human traits.