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Asgrow Seed Company, 634 Lincolnway, Ames, IA 50010
Univ. of Illinois, S112 Turner Hall, 1102 South Goodwin Ave., Urbana, IL 61801
ICI Seeds, 2369 330th Street, Slater, IA 50244
* Corresponding author (email jdudley{at}uiuc.edu).
Early generation testing allows the discarding of lines with poor combining ability early in the inbreeding process; however, maintaining a high probability of retaining superior performing lines at homozygosity requires a low selection intensity in early generations. This study was conducted to determine the usefulness of marker associated effects estimated from early generation testcross data for predicting later generation testcross performance. From the maize (Zea mays L.) cross BSIl(FR)C7xFRMo17, 190 random families represented in the S1 and S4:5 generations were testcrossed to an elite B73 type inbred. Families were genotyped at 157 marker loci in both generations. Models using S1 testcross data, net molecular scores, and indices combining phenotypic and molecular information were evaluated for their ability to predict S4:5 testcross performance. for both grain yield and percent stalk lodging, an index including both phenotypic and marker information predicted S4:5 testcross performance better than either phenotypic or marker information alone. For grain yield, combining marker information with phenotypic information allowed a reduction of 40% in the number of lines tested in the S4:5. Because of the high heritabilities in this study, phenotypic informationp redicted the top S4:5 testcrosses better than markerin formation alone. Because of the high heritability for grain moisture, combining marker information with phenotypic information did not improve predictability. Adding marker information to phenotypic information also improved prediction of S4:5 testcross performance for an index of multiple traits. Markemr odels for S1 testcross grain yield developed for a set of high yielding environments contained different marker loci than models developed for a set of low yielding environments. For models developed from the high yielding environments, marker information was able to improve selection of the top S4:5 families over selection based on phenotypic data.
Received for publication January 6, 1996.
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