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Quantitative Trait Loci (QTL)

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Cover of 'Quantitative Trait Loci (QTL)'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Backcross Populations and Near Isogenic Lines
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    Chapter 2 F2 Designs for QTL Analysis
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    Chapter 3 Design and Construction of Recombinant Inbred Lines
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    Chapter 4 Two Flavors of Bulk Segregant Analysis in Yeast
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    Chapter 5 Selecting markers and evaluating coverage.
  7. Altmetric Badge
    Chapter 6 Composite Interval Mapping and Multiple Interval Mapping: Procedures and Guidelines for Using Windows QTL Cartographer
  8. Altmetric Badge
    Chapter 7 Design Database for Quantitative Trait Loci (QTL) Data Warehouse, Data Mining, and Meta-Analysis
  9. Altmetric Badge
    Chapter 8 Meta-analysis of QTL Mapping Experiments
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    Chapter 9 Using eQTLs to Reconstruct Gene Regulatory Networks
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    Chapter 10 Estimation and Interpretation of Genetic Effects with Epistasis Using the NOIA Model
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    Chapter 11 Identifying QTL for Multiple Complex Traits in Experimental Crosses
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    Chapter 12 Functional Mapping of Developmental Processes: Theory, Applications, and Prospects
  14. Altmetric Badge
    Chapter 13 Statistical Models for Genetic Mapping in Polyploids: Challenges and Opportunities
  15. Altmetric Badge
    Chapter 14 eQTL
  16. Altmetric Badge
    Chapter 15 Genetic Mapping of Quantitative Trait Loci for Disease-Related Phenotypes
  17. Altmetric Badge
    Chapter 16 Quantitative Trait Locus Analysis in Haplodiploid Hymenoptera
Attention for Chapter 5: Selecting markers and evaluating coverage.
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Chapter title
Selecting markers and evaluating coverage.
Chapter number 5
Book title
Quantitative Trait Loci (QTL)
Published in
Methods in molecular biology, March 2012
DOI 10.1007/978-1-61779-785-9_5
Pubmed ID
22565833
Book ISBNs
978-1-61779-784-2, 978-1-61779-785-9
Authors

Matthew A. Cleveland, Nader Deeb

Editors

Scott A. Rifkin

Abstract

The availability of genetic markers in many species has enabled the analysis of marker-trait associations ranging from small genomic regions to genome-wide scale. An appropriate set of markers must be identified to meet the objectives of any research, using a custom discovery and selection approach or by using a commercial product. The key considerations in selecting markers are the quantity and the distribution across the genome. Though decisions about how many markers to use are often pragmatic, influenced by costs and available technology, an evaluation of the marker coverage is important in understanding how to design an effective genomic research study with reasonable expectations about the power to obtain desired results. An important parameter to evaluate coverage is linkage disequilibrium, which can be used to determine the appropriate number of markers for a particular analysis and is related to the proportion of variance that can be explained by a given marker, or power. Finally, the type of analysis used to identify marker-trait associations may depend on marker coverage as the optimal approach, from a statistical or computational standpoint, may differ with changes in marker number and distribution.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 57%
Student > Master 3 21%
Researcher 1 7%
Unknown 2 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 64%
Environmental Science 1 7%
Engineering 1 7%
Unknown 3 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 May 2012.
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#18,305,773
of 22,664,644 outputs
Outputs from Methods in molecular biology
#7,815
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#123,896
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Outputs of similar age from Methods in molecular biology
#34
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