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Statistical Human Genetics

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Cover of 'Statistical Human Genetics'

Table of Contents

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    Book Overview
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    Chapter 1 Statistical Genetic Terminology
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    Chapter 2 Identification of Genotype Errors
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    Chapter 3 Detecting Pedigree Relationship Errors
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    Chapter 4 Identifying Cryptic Relationships
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    Chapter 5 Estimating Allele Frequencies
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    Chapter 6 Testing Departure from Hardy-Weinberg Proportions
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    Chapter 7 Estimating Disequilibrium Coefficients
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    Chapter 8 Detecting Familial Aggregation
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    Chapter 9 Estimating Heritability from Twin Studies
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    Chapter 10 Estimating Heritability from Nuclear Family and Pedigree Data
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    Chapter 11 Correcting for Ascertainment
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    Chapter 12 Segregation Analysis Using the Unified Model
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    Chapter 13 Design Considerations for Genetic Linkage and Association Studies
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    Chapter 14 Model-Based Linkage Analysis of a Quantitative Trait
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    Chapter 15 Model-Based Linkage Analysis of a Binary Trait
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    Chapter 16 Model-Free Linkage Analysis of a Quantitative Trait
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    Chapter 17 Model-Free Linkage Analysis of a Binary Trait
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    Chapter 18 Single Marker Association Analysis for Unrelated Samples
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    Chapter 19 Single Marker Family-Based Association Analysis Conditional on Parental Information
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    Chapter 20 Single Marker Family-Based Association Analysis Not Conditional on Parental Information
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    Chapter 21 Calibrating Population Stratification in Association Analysis
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    Chapter 22 Cross-Phenotype Association Analysis Using Summary Statistics from GWAS
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    Chapter 23 Haplotype Inference
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    Chapter 24 Multi-SNP Haplotype Analysis Methods for Association Analysis
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    Chapter 25 The Analysis of Ethnic Mixtures
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    Chapter 26 Detecting Multiethnic Rare Variants
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    Chapter 27 Identifying Gene Interaction Networks
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    Chapter 28 Structural Equation Modeling
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    Chapter 29 Mendelian Randomization
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    Chapter 30 Preprocessing and Quality Control for Whole-Genome Sequences from the Illumina HiSeq X Platform
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    Chapter 31 Processing and Analyzing Human Microbiome Data
Attention for Chapter 14: Model-Based Linkage Analysis of a Quantitative Trait
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Chapter title
Model-Based Linkage Analysis of a Quantitative Trait
Chapter number 14
Book title
Statistical Human Genetics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7274-6_14
Pubmed ID
Book ISBNs
978-1-4939-7273-9, 978-1-4939-7274-6
Authors

Yeunjoo E. Song, Sunah Song, Audrey H. Schnell

Abstract

Linkage Analysis is a family-based method of analysis to examine whether any typed genetic markers cosegregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single-marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it also includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single-marker analysis, and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

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Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 25%
Other 1 25%
Unknown 2 50%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 25%
Neuroscience 1 25%
Unknown 2 50%