Genomic selection to optimize doubled haploid-based hybrid breeding in maize

Abstract

Crop improvement, as a long-term endeavor, requires continuous innovations in technique from multiple perspectives. Doubled haploid (DH) technology for pure inbred production, which shaves years off of the conventional selfing approach, has been widely used for breeding. However, the final success rate of in vivo maternal DH production is determined by four factors: haploids induction, haploids identification, chromosome doubling, and successful selfing of the fertile haploid plants to produce DH seeds. Traits in each of these steps, if they can be accurately predicted using genomic selection methods, will help adjust the DH production protocol and simplify the logistics and save costs. Here, a hybrid population (N=158) was generated based on an incomplete half diallel design using 27 elite inbred lines. These hybrids were induced to create F1-derived haploid families. The hybrid materials, as well as the 27 inbreds, the inbred-derived haploids (N=200), and the F1-derived haploids (N=5,000) were planted in the field to collect four DH-production traits, three yield-related traits, and three developmental traits. Quantitative genetics analysis suggested that in both diploids and haploid families, most of the developmental traits showed high heritability, while the DH-production and developmental traits exhibited intermediate levels of heritability. By employing different genomic selection models, our results showed that the prediction accuracy ranged from 0.52 to 0.59 for the DH-production traits, 0.50 to 0.68 for the yield-related traits, and 0.44 to 0.87 for the developmental traits. Further analysis using index selection achieved the highest prediction accuracy when considering both DH production efficiency and the agronomic trait performance. Furthermore, the long-term responses through simulation confirmed that index selection would increase the genetic gain for targeted agronomic traits while maintaining the DH production efficiency. Therefore, our study provides an optimization strategy to integrate GS technology for DH-based hybrid breeding.

Androgenesis-Based Doubled Haploidy: Past, Present, and Future Perspectives

“…Combining DHs and marker-assisted selection (MAS) has resulted in a significant advancement of the breeding systems in many major crops (Tuvesson et al, 2007). Genomic selection (GS), as a recently emerged technology to predict the performance of plants without phenotyping, has proven to be effective in plant breeding (Li et al, 2020;Krishnappa et al, 2021).…”

“…DH methods have been, and are being, used to accelerate the breeding programs in a variety of crop plants, including maize (Zea mays), barley (Hordeum vulgare), Brassica sp., wheat (Triticum aestivum), and several vegetable crops (Ho and Jones, 1980;Ferrie and Möllers, 2011;Krishnappa et al, 2021). Maize is a crop where such an increase in improvement efficiency has resulted in a significant increase in genetic gain (Li et al, 2020). Geiger et al (2013) reported that genetic variances and per se performance were substantially increased in haploids and DH in maize.…”

“…This allows the best opportunity to use scientific knowledge generated through DH technology for several years in crops [e.g., barley, Brassica sp., maize, rice (Oryza sativa), and wheat]s, and integrate them with phenomics and genomics to accelerate breeding pipelines. Li et al (2020) simulated DH-based genomic selection procedures and reported that it is possible to obtain substantial long-term genetic gain along with the feasibility of increasing multiple traits simultaneously (Brauner et al, 2019).…”

“…This eliminates the need for handling larger numbers of breeding materials from different generations of inbreeding. In other words, significant field resources are saved by allowing smaller population sizes to produce a homozygous (fixed) trait, and/or evaluation of better performing lines (Park et al, 1976;Ho and Jones, 1980;Dwivedi et al, 2015;Li et al, 2020). In addition to the advantage of evaluating all the possible recombinants together, logistics like shipping the seed, managing inventories, planting nurseries, selfing, and maintaining lines are simplified (Röber et al, 2005;Prasanna et al, 2012;Chaikam et al, 2019).…”

Multi-Trait Genomic Prediction Improves Accuracy of Selection among Doubled Haploid Lines in Maize

“…Empirical studies for predicting field performances of DH lines using genomic prediction have been reported in maize [ 6 , 7 , 8 , 9 ]. These studies demonstrated the usefulness of genomic prediction for preselection of DH lines in the early stage of a breeding pipeline.…”