B. Genomic Selection (GS)

Genomic Selection (GS) is a promising an active research area where genomics technologies are well integrated into plant and animal improvement practices. The great interest in GS has been clearly demonstrated by the rapid surge of journal publications and citations in plants and animals. Array-based genotyping has also been established in many crop and animal species, facilitating the application of GS in plant and animal improvement. More recently, Genotyping-by-Sequencing (GBS) significantly reduced the cost of genotyping, making regular GS of complex traits possible in many research programs.

Besides embedding prediction procedure in the actual breeding populations, various other applications of GS include cultivar/inbred/individual performance prediction, hybrid combination prediction, parental selection, germplasm utilization, across environment prediction, and wild introgression.

* Bernardo and Yu 2007 was recognized as a milestone paper in Plant Breeding in the recent review at Nature Biotechnology, Breeding Crops to Feed 10 Billion.

Please check out this YouTube, Genomic selection: Historical context, technical details, empirical findings, and perspectives. It is not helpful to view, present, and learn Genomic Selection as a highly technical model building process, which, unfortunately, has been done by many. Genomic Selection should be explained as a natural extension of decades of plant breeding effort to incorporate different enabling tools and strategies.

(Note: Thanks to the graduate students at Texas A&M, this talk was given at Texas A&M Plant Breeding Symposium, Texas A&M University, Feb. 19, 2015, College Station, TX.)

Here is a recent paper from our group, Genomic Prediction Contributing to a Promising Global Strategy to Turbocharge Gene Banks (Nature Plants, 2016, 2:16150)

To understand the broad context of different methods and detailed derivations, please refer to this very nice review from Gota Morota and Deniel Gianola. It tells the whole thing, not just "kernel-based". (HIGHLY RECOMMENDED READING)

Kernel-based whole-genome prediction of complex traits: a review (Frontiers in Genetics, 2014, 5,00363)

Some well cited research and review papers on GS.

Prediction of total genetic value using genome-wide dense marker maps (Genetics, 2001, 157:1819-1829)

Prospects for genomewide selection for quantitative traits in maize (Crop Science, 2007, 47:1082-1090)

Genomic selection for crop improvement (Crop Science, 2009, 49:1-12)

Will genomic selection be a practical method for plant breeding? (Annals of Botany, 2012, 110:1303-1316)

Genomic selection: genome-wide prediction in plant improvement (Trends in Plant Sciences, 2014, 19:592–601)

Some interesting research papers on GS.

Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize (Genetics, 2014, 197:1343-1355)

Note: Proposing a paradigm shift in hybrid breeding away from testcross evaluation.

Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods ... (Genetics, 2012, 192:715-728)

Note: Proposing a optimization of training population.

Selection on optimal haploid value increases genetic gain and preserves more genetic diversity relative to genomic selection (Genetics, 2015, 200:1341-1348)

Note: Proposing OPV as the selection standard rather than GEBV to maintain diversity and increase long-term genetic gain.

Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize (Theoretical and Applied Genetics, 2016, 129:2413-2427)

Metabolomic prediction of yield in hybrid rice (Plant Journal, 2016, 88:219–227)

Note: These two papers explored the use of transcriptomic and metabolomic data for GS, and the comparison and integration with genomic SNPs.

A pair of perspective papers with a focus in human genetics

Pitfalls of predicting complex traits from SNPs (Nature Review Genetics, 2013, 14:507-515)

Predicting genetic predisposition in humans: the promise of whole-genome markers (Nature Review Genetics, 2010, 11:880-886)

Notes:

A very nice overview was presented by Dorian Garrick at the 2013 PAG GS+GWAS workshop. Check out the pdf of the presentation.

Current Status Of Genomic Prediction and GWAS In Livestock

Department of Animal Science at ISU has summer short courses on GS and related topics at ISU campus.