Premium
Marker‐Assisted Best Linear Unbiased Prediction of Single‐Cross Performance
Author(s) -
Bernardo Rex
Publication year - 1999
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/cropsci1999.3951277x
Subject(s) - best linear unbiased prediction , trait , quantitative trait locus , heritability , biology , statistics , hybrid , selection (genetic algorithm) , genetics , mathematics , computer science , agronomy , gene , machine learning , programming language
Predicting the performance of untested single crosses is important in hybrid breeding programs. The objective of this study was to compare the effectiveness of best linear unbiased prediction based on trait data alone (T‐BLUP) and trait and marker data combined (TM‐BLUP). The simulation procedure involved creating founder and recombinant inbreds in each of two heterotic groups, determining genetic and phenotypic values of 3025 single crosses, randomly partitioning the single crosses into 500 tested and 2525 untested hybrids, and calculating the correlation between the true and predicted performance of untested single crosses. The T‐BLUP correlations ranged from 0.74 to 0.84, with n = 10, 50, or 100 quantitative trait loci (QTL) and trait heritability of 0.4 or 0.6. The advantage of TM‐BLUP over T‐BLUP decreased as n increased. With n = 50 or 100, the TM‐BLUP correlations exceeded the T‐BLUP correlations by 0.00 to 0.03, even when all QTL were tightly linked to flanking markers. The usefulness of TM‐BLUP is doubtful, not only for predicting single‐cross performance, but also for predicting breeding values of individuals within populations. The TM‐BLUP procedure is useful when few QTL control a trait, or when genetic gain is sought only at a limited subset of QTL.