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a Groningen Bioinformatics Centre, Inst. of Mathematics and Computing Sci., POB 800, NL-9700 AV, Groningen, The Netherlands
b Dep. of Agronomy, Iowa State Univ., Ames, IA 50011-1010, USA
c NCGR, 2935 Rodeo Park Drive East, Santa Fe, NM 87505, USA
* Corresponding author (r.c.jansen{at}cs.rug.nl)
Applied breeding programs evaluate large numbers of progeny derived from multiple related crosses for a wide range of agronomic traits and for tens to hundreds of molecular markers. This study was conducted to determine how these phenotypic and genetic data could be used for routinely mapping quantitative trait loci (QTLs). With dense maps, haplotype sharing of parents in a certain region is a good indicator for QTL-allele sharing, albeit not 100% perfect. With this in mind, an approximate and simple method has been developed where ancestral genome blocks in the parents of the crosses can be identified via haplotype analysis and where the effect of a putative QTL is then modeled and estimated per ancestral genome block. A simulation of an early-generation maize breeding scheme demonstrates the potential of the present approach for QTL detection in existing breeding programs. With this new QTL mapping strategy, the power, precision, and accuracy associated with large numbers of progeny may be attained, inferences about QTLs can be drawn across the breeding program rather than being limited to the sample of progeny from a single cross, and results may be much more valuable for marker-assisted breeding because the QTLs apply to agronomically challenging situations in the field.
Abbreviations: DH, doubled haploid • haploIM, haplotype interval mapping • haploMQM, haplotype multiple quantitative trait loci model • haploMQM-, reduced haplotype multiple quantitative trait loci model • IM, interval mapping • MQM, multiple quantitative trait loci model • QTL, quantitative trait loci
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