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Genomewide Selection versus Marker‐assisted Recurrent Selection to Improve Grain Yield and Stover‐quality Traits for Cellulosic Ethanol in Maize
Author(s) -
Massman Jon M.,
Jung HansJoachim G.,
Bernardo Rex
Publication year - 2013
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2012.02.0112
Subject(s) - stover , biology , selection (genetic algorithm) , population , index selection , microbiology and biotechnology , agronomy , zoology , crop , medicine , environmental health , artificial intelligence , computer science
Genomewide selection (GWS) is marker‐assisted selection without identifying markers with significant effects. Our previous work with the intermated B73 × Mo17 maize ( Zea mays L .) population revealed significant variation for grain yield and stover‐quality traits important for cellulosic ethanol production. Our objectives were to determine (i) if realized gains from selection are larger with GWS than with marker‐assisted recurrent selection (MARS), which involves selection for markers with significant effects; and (ii) how multiple traits respond to multiple cycles of GWS and MARS. In 2007, testcrosses of 223 recombinant inbreds developed from B73 × Mo17 (Cycle 0) were evaluated at four Minnesota locations and genotyped with 287 single nucleotide polymorphism markers. Individuals with the best performance for a Stover Index and a Yield + Stover Index were intermated to form Cycle 1. Both GWS and MARS were then conducted until Cycle 3. Multilocation trials in 2010 indicated that gains for the Stover Index and Yield + Stover Index were 14 to 50% larger (significant at P = 0.05) with GWS than with MARS. Gains in individual traits were mostly nonsignificant. Inbreeding coefficients ranged from 0.28 to 0.38 by Cycle 3 of GWS and MARS. For stover‐quality traits, correlations between wet chemistry and near‐infrared reflectance spectroscopy predictions decreased after selection. We believe this is the first published report of a GWS experiment in crops, and our results indicate that using all available markers for predicting genotypic value leads to greater gain than using a subset of markers with significant effects.