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Title: Recent progress in alfalfa (Medicago sative L.) genomics and genomic selection

item HAWKINS, CHARLES - Washington State University
item Yu, Long-Xi

Submitted to: The Crop Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/30/2018
Publication Date: 3/7/2018
Citation: Hawkins, C., Yu, L. 2018. Recent progress in alfalfa (Medicago sative L.) genomics and genomic selection. The Crop Journal.

Interpretive Summary: Alfalfa is a legume forage crop grown primarily for hay and silage. In 2016, the United States produced 58,263,000 tons of alfalfa. Cultivated alfalfa (Medicago sativa ssp. sativa) is an outcrossing autotetraploid with a basic chromosome number of eight. Alfalfa plants are highly heterozygous and exhibit severe inbreeding depression, precluding the development of inbreed lines. It is a considerable challenge to genotype individuals with such a complicated genome. However, recent advances in next generation sequencing have provided a new strategy to generate cost-effective, high-density, genome-wide single nucleotide polymorphism (SNP) sets. In conjunction with genome-wide association studies (GWAS) and/or genomic selection (GS), more powerful platforms can be developed to improve gains in alfalfa breeding. This paper discribed the methods, considerations, and recent progress in alfalfa genomics and Genomic Selection.

Technical Abstract: Alfalfa is a highly valuable forage crop, providing more than 58 million tons of hay, silage, and pasture each year in the United States. As alfalfa is an outcrossing tetraploid crop, however, breeding for enhanced agronomic traits is challenging and progress has historically not been rapid. Methods that utilize genotypic information and statistical models to generate a genomic estimated breeding value (GEBV) for each plant at a young age hold a great deal of promise to accelerate breeding gains. An emerging breeding pipeline is genotyping-by-sequencing (GBS) to identify SNP markers in a training population, followed by a genome-wide association study (GWAS) to find associations between the discovered SNPs and traits of interest, followed by genomic selection (GS), a breeding program utilizing the trained model to predict breeding values and making selections based on them. Much work has been done in recent years in all of these areas, to generate marker sets and discover SNPs associated with desirable traits, and alfalfa breeding programs utilizing these techniques are underway. At the same time, GBS/GWAS/GS is still a new pipeline, and work is ongoing to evaluate different models, software, and methods for use in such programs. In this paper, we look at the progress of alfalfa genomics over the past half-decade, and review work comparing models and methods relevant to this new type of breeding pipeline.