This is the 14th module in a series of 17 developed by the Conifer Translational Genomics Network (CTGN) and Pine Reference Sequence (Pine RefSeq). This module by CTGN focuses on prediction of breeding values using molecular markers in the context of forest trees.
This module is organized in four sections. The first section focuses on best linear unbiased prediction (BLUP) of breeding values using a numerical relationship matrix, called the A matrix. This matrix is usually derived from pedigrees of individuals. The relationships are sometimes called additive genetic covariances. The second section focuses on incorporation of markers in models and the various approaches to marker aided selection we have introduced in previous modules. The third section addresses a key issue related to the use of markers for selection, namely, how missing genotypes are inferred or imputed so that breeding values can be calculated. The final section focuses on the realized genetic matrix for G-BLUP. The genetic relationship matrix based on the markers is called the G matrix, and BLUP analysis based on the G matrix is called G-BLUP.
Module 14 — Using Markers to Predict Breeding Values
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- Hayes, B. QTL mapping, MAS, and genomic selection. [Online short course notes]. 2007 Animal Breeding & Genetics Short Courses, Iowa State Unversity. Available at: http://http://www.ans.iastate.edu/stud/courses/short/2007/) (verified 19 Apr 2012).
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Cite This Learning Module
- Isik, F. Using markers to predict breeding value. [Online Learning Module]. Genomics in Tree Breeding and Forest Ecosystem Management, Conifer Translational Genomics Network. eXtension Foundation. Available at http://www.extension.org/pages/63412 (verified April 22, 2013).
- Fikret Isik developed the learning module content.
- Heather Merk developed the webpage.
Support for the Conifer Translational Genomics Network project and the development of the teaching modules hosted here was provided by the USDA/NRI CSREES Plant Genomics Coordinated Agricultural Project (CAP) Award # 2007-55300-18603, the USDA/NIFA AFRI Applied Plant Genomics CAP Award #2009-85606-05680 and the USDA Forest Service. Development of this page was supported in part by the National Institute of Food and Agriculture (NIFA) Solanaceae Coordinated Agricultural Project, agreement 2009-85606-05673, administered by Michigan State University. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the United States Department of Agriculture.