Genomic Selection: Conifer Genomics Module 15

Plant Breeding and Genomics July 26, 2013 Print Friendly and PDF

Authors:

Ross Whetten, North Carolina State University; Heather L. Merk, The Ohio State University

This is the 15th module in a series of 16 developed by the Conifer Translational Genomics Network (CTGN) and Pine Reference Sequences (PineRefSeq). This module by CTGN focuses on genomic selection.

Introduction

Genomic selection has developed, since the mid 1990s, from a hypothetical concept to a practical way of applying genetic marker data in breeding. As the name suggests, the objective of genomic selection is to provide breeders with a method of making selections in breeding programs. This stands in contrast to statistical approaches, such as association genetics, that were initially developed to identify specific genes, or even specific DNA sequence variants as the causative agents underlying phenotypic variation.

This module provides an historic perspective of genomic selection and an overview of different approaches to the computational problems of estimating breeding value from marker genotypes. Successful application of genomic selection to dairy cattle breeding is presented. The module also includes discussion of strategies that may be useful in applying genomic selection to forest tree breeding.

Module Screen Shot and Link

 

Module 15 — Genomic Selection

See other Conifer Genomics Modules

You can also watch the video on YouTube

 

References Cited

  • Chapman, N. H. and E. A. Thompson. 2002. The effect of population history on the lengths of ancestral chromosome segments. Genetics 162: 449-458.
  • Eberle, M.A., M. J. Rieder, L. Kruglyak, and D. A. Nickerson. 2006. Allele frequency-matching between SNPs reveals an excess of linkage disequilibrium in genic regions of the human genome. Public Library of Science Genetics 2: e142. (Available online at: http://dx.doi.org/10.1371/journal.pgen.0020142) (verified 19 Apr 2012).
  • Grattapaglia, D and M. D. V. Resende. 2011. Genomic selection in forest tree breeding. Tree Genetics & Genomes 7: 241-255.
  • Haley, C.S. and P. M. Visscher. 1998. Strategies to utilize marker-quantitative trait loci associations. Journal of Dairy Science 81(Supp2): 85-97. (Available online at: http://dx.doi.org/10.3168/jds.S0022-0302(98)70157-2) (verified 19 Apr 2012).
  • Kühn, C., G. Thaller, A. Winter, O. R. Bininda-Emonds, B. Kaupe, G. Erhardt, J. Bennewitz, M. Schwerin, and R. Fries. 2004. Evidence for multiple alleles at the DGAT1 locus better explains a quantitative trait locus with major effect on milk fat content in cattle. Genetics 167: 1873–1881. (Available online at: http://dx.doi.org/10.1534/genetics.103.022749) (verified 19 Apr 2012).
  • Meuwissen , T.H.E., B. J. Hayes, and M. E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819-1829.
  • Meuwissen, T.H.E. and M. E. Goddard. 2010. Accurate prediction of genetic values for complex traits by whole-genome resequencing . Genetics 185: 623-631. (Available online at: http://dx.doi.org/10.1534/genetics.110.116590) (verified 19 Apr 2012).
  • Powell, J. E., P. M. Visscher, and M. E. Goddard. 2010. Reconciling the analysis of IBD and IBS in complex trait studies. Nature Reviews Genetics 11: 800-805. (Available online at: http://dx.doi.org/10.1038/nrg2865) (verified 19 Apr 2012).
  • Shoemaker, J. S., I. S. Painter, and B. S. Weir. 1999. Bayesian statistics in genetics: a guide for the uninitiated. Trends in Genetics 15: 354-358. (Available online at: http://dx.doi.org/10.1016/S0168-9525(99)01751-5) (verified 19 Apr 2012).
  • Stein, L. D. 2010. The case for cloud computing in genome informatics. Genome Biology 11: 207. (Available online at: http://dx.doi.org/10.1186/gb-2010-11-5-207) (verified 19 Apr 2012).
  • VanRaden, P. M., C. P. Van Tassell, G. R. Wiggans, T. S. Sonstegard, R. D. Schnabel, J. F. Taylor, and F. S. Schenkel. 2009. Invited review: Reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92: 16–24. (Available online at: http://dx.doi.org/10.3168/jds.2008-1514) (verified 19 Apr 2012).

Cite This Learning Module

  • Whetten, R. Genomic selection [Online Learning Module]. Genomics in Tree Breeding and Forest Ecosystem Management, Conifer Translational Genomics Network. eXtension Foundation. Available at:  http://www.extension.org/pages/63413 (verified April 22, 2013).

Author Contributions

  • Ross Whetten developed the learning module content.

  • Heather Merk developed the webpage.

Funding Statement

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.

Attachments:

OLM_15_GenomicSelection_FINAL.pdf (607.18 KB)

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This work is supported by the USDA National Institute of Food and Agriculture, New Technologies for Ag Extension project.