<mets:mets OBJID="eprint_10356" LABEL="Eprints Item" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mets="http://www.loc.gov/METS/" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mets:metsHdr CREATEDATE="2023-07-05T00:46:55Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>OAR@ICRISAT</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_10356_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>Genomic Selection for Crop Improvement: An Introduction</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">R K</mods:namePart><mods:namePart type="family">Varshney</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">M</mods:namePart><mods:namePart type="family">Roorkiwal</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">M E</mods:namePart><mods:namePart type="family">Sorrells</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Marker-assisted selection (MAS) exploits the markers associated with traits of interest for selecting lines with superior alleles for developing improved lines. However use of MAS is restricted to simple traits due to its inability to handle complex traits. Advancements in genomics technologies have been able to dramatically reduce the cost of genotyping, enabling the use of genome-wide marker data for selecting lines with higher breeding value. Genomic selection (GS), a modern breeding approach that uses genome-wide marker data to estimate the breeding value and has the potential to address the complex traits. GS exploits the genotyping and phenotyping data on a training population to train the prediction models to calculate the genomic estimated breeding value (GEBV). GS has the capability to reduce selection cycle duration and increase selection accuracy, intensity, efficiency, and gains per unit of time, thereby enhancing the rate of genetic gains. Availability of cost-effective genotyping platforms has enabled the cost-effective generation of large-scale genotyping data, facilitating the deployment of GS in several crop species. This chapter provides an introduction to the book, highlighting the basic and advanced principles of GS breeding and its applications for crop improvement.</mods:abstract><mods:classification authority="lcc">Molecular Biology</mods:classification><mods:classification authority="lcc">Genetics and Genomics</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2017-11-01</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Springer International Publishing</mods:publisher></mods:originInfo><mods:genre>Book Section</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_10356"><mets:rightsMD ID="rights_eprint_10356_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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