Doubled haploids in hybrid maize breeding.

Abstract

Use of doubled haploid (DH) lines produced by in vivo induction of maternal haploids are routinely used in maize (Zea mays L.) breeding. Major advantages of DH lines in hybrid breeding are (i) maximum genetic variance, (ii) complete homozygosity, (iii) short time to market, (iv) simplified logistics, (v) reduced expenses, and (vi) optimal aptitude for marker applications. The present paper briefly reviews the experimental basis of the haploid induction technology, explains alternative DH-line-based breeding schemes, describes the features of a new software for optimizing such schemes, and presents and discusses selected optimization results. Modern inducer genotypes display induction rates of 8 to 10% on average. Various morphological and physiological markers warrant a fast and cheap identification of haploid kernels and/or seedlings. Artificial chromosome doubling procedures have successfully been adapted to large-scale commercial applications. Most likely, haploid embryogenesis is caused by defective sperm cells. After fusion with the egg cell, the chromosomes of the sperm cell degenerate and are stepwise eliminated from the primordial cells. The induction rate is under polygenic control. One cycle of DH-line development with two stages of testcross evaluation takes only four years if off-season nurseries are available. Cycle length can be shortened to three years if the first three breeding steps (recombination, haploid induction, and DH-plant production) are completed in a single year. Genome-wide marker-assisted selection can effectively be incorporated into DH-line based breeding schemes. To maintain selection response in the long run, the loss of genetic variation needs to be minimized by setting lower limits to the effective population size (N e ). Recurrent selection and line development may be combined to a single integrated breeding scheme. A new software MBP (Version 1.0) maximizes the expected annual genetic gain subject to budget and N e restrictions. Input variables include estimated variance and covariance components, type of tester, haploid induction parameters, and costs of the individual breeding activities. For calculating N e , the software considers genetic drift caused by both sampling and selection. Optimization results demonstrate that (i) schemes with only one stage of testcross evaluation provide faster breeding progress than those with two or more stages, (ii) genetic interlinking between staggered breeding programs is more efficient than a closed-population approach. Combined phenotypic and genome-wide selection holds great promise in accelerating future breeding progress.